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BEGIN:VEVENT
UID:ai1ec-2677@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:Canary Center
CONTACT:Ashley Williams\; ashleylw@stanford.edu\; https://www.earlydetectio
nresearch.com/
DESCRIPTION:Cancer Research UK\, OHSU Knight Cancer Institute and the Canar
y Center at Stanford\, present the Early Detection of Cancer Conference se
ries. The annual Conference brings together experts in early detection fro
m multiple disciplines to share ground breaking research and progress in t
he field.\nThe Conference is part of a long-term commitment to invest in e
arly detection research\, to understand the biology behind early stage can
cers\, find new detection and screening methods\, and enhance uptake and a
ccuracy of screening.\nThe 2021 conference will take place October 6-8 vir
tually. For more information visit the website: http://earlydetectionresea
rch.com/\nTickets: https://www.earlydetectionresearch.com/virtual-experien
ce/.
DTSTART;VALUE=DATE:20211006
DTEND;VALUE=DATE:20211009
LOCATION:Virtual Event
SEQUENCE:0
SUMMARY:Early Detection of Cancer Conference
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/early-de
tection-of-cancer-conference-2/
X-COST-TYPE:external
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
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ent/uploads/2019/10/EDx21_300x300.jpg\;300\;300\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n
\\n\\n\\n
Cancer Research UK\, OHSU Knight Cancer
Institute and the Canary Center at Stanford\, present the Early Detection
of Cancer Conference series. The annual Conference brings together experts
in early detection from multiple disciplines to share ground breaking res
earch and progress in the field.
\n
The Conference is part of a long-
term commitment to invest in early detection research\, to understand the
biology behind early stage cancers\, find new detection and screening meth
ods\, and enhance uptake and accuracy of screening.
X-TICKETS-URL:https://www.earlydetectionresearch.com/virtual-experience/
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1509@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND Seminar Series: Impact of the Veterans Affairs National A
bdominal Aortic Screening Program\nManuel Garcia-Toca\, M.D.\nClinical Pro
fessor of Surgery\nChief\, Division of Vascular Surgery\nSanta Clara Vall
ey Medical Center (SCVMC)\n \nOliver O. Aalami\, M.D.\nClinical Associate
Professor of Surgery\, Vascular Surgery\nLucile Packard Children’s Hospita
l\n \nLocation: Zoom\nWebinar URL: https://stanford.zoom.us/s/98417624095
\nDial: US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID: 9
84 1762 4095\nPasscode: 111283\n11:00am – 12:00pm Seminar & Discussion\nRS
VP Here\n \nABSTRACT\nBackground: The U.S. Federal Government enacted the
Screen for Abdominal Aortic Aneurysms Very Efficiently Act in January 2007
. Simultaneously\, the Department of Veterans Affairs (VA) implemented a m
ore inclusive AAA screening policy for veteran beneficiaries shortly after
wards.\n \nOur study aimed to evaluate the impact of the VA program on AAA
detection rate and all-cause mortality compared to a cohort of patients w
hose aneurysms were identified by other abdominal imaging.\n \nMethods: We
identified veterans with an AAA screening study using the two existing Cu
rrent Procedural Terminology (CPT) codes (G0389 and 76706). In the compar
ison group\, eligible abdominal imaging studies included ultrasound\, comp
uted tomography (CT)\, and magnetic resonance imaging (MRI) queried accord
ing to CPT codes between 2001 and 2018.\n \nWe used a difference-in-differ
ences regression model to evaluate the change in aneurysm detection rate a
nd all-cause mortality five years before and eleven years after the VA imp
lemented the screening policy in 2007.\n \nWe calculated survival estimate
s after AAA screening or non-screening imaging of patients with or without
AAA diagnosis and used multivariate Cox regression model to evaluate mort
ality in patients with a positive AAA diagnosis adjusting for patient char
acteristics and comorbidities.\n \nResults: We identified 3.9 million vete
rans with abdominal imaging\, a total of 303\,664 of whom were coded has h
aving an AAA US screening between 2007 and 2018. An AAA diagnosis was made
in 4.84% of the screening group vs. 1.3% in the non-screening imaging gro
up P
DTSTART;TZID=America/Los_Angeles:20210420T110000
DTEND;TZID=America/Los_Angeles:20210420T120000
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:PHIND Seminar – Manuel Garcia-Toca\, M.D. & Oliver O. Aalami\, M.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-manuel-garcia-toca-m-d-oliver-o-aalami-m-d/
X-COST-TYPE:external
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Serie
s: Impact of the Veterans Affairs National Abdominal Aortic Screening
Program
\n
Manuel Garcia-Toca\, M.D. \nClinical Profe
ssor of Surgery \nChief\, Division of Vascular Surgery \nSanta
Clara Valley Medical Center (SCVMC)
\n
\n
Oliver O. Aalami\, M.D. \nClinical Associate Professor of Surgery\, Vascular Surgery \nLucile Packard Children’s Hospital
11:00am – 12:00pm Seminar &
Discussion \nRSVP Here
\n
\n
ABSTRACT
\n
Background: The U.S. Federal
Government enacted the Screen for Abdominal Aortic Aneurysms Very Efficie
ntly Act in January 2007. Simultaneously\, the Department of Veterans Affa
irs (VA) implemented a more inclusive AAA screening policy for veteran ben
eficiaries shortly afterwards.
\n
\n
Our study aimed to evalua
te the impact of the VA program on AAA detection rate and all-cause mortal
ity compared to a cohort of patients whose aneurysms were identified by ot
her abdominal imaging.
\n
\n
Methods: We iden
tified veterans with an AAA screening study using the two existing Cur
rent Procedural Terminology (CPT) codes (G0389 and 76706). In the co
mparison group\, eligible abdominal imaging studies included ultrasound\,
computed tomography (CT)\, and magnetic resonance imaging (MRI) queried ac
cording to CPT codes between 2001 and 2018.
\n
\n
We used a di
fference-in-differences regression model to evaluate the change in aneurys
m detection rate and all-cause mortality five years before and eleven year
s after the VA implemented the screening policy in 2007.
\n
\n
We calculated survival estimates after AAA screening or non-screening ima
ging of patients with or without AAA diagnosis and used multivariate Cox r
egression model to evaluate mortality in patients with a positive AAA diag
nosis adjusting for patient characteristics and comorbidities.
\n
p>\n
Results: We identified 3.9 million veterans with a
bdominal imaging\, a total of 303\,664 of whom were coded has having an AA
A US screening between 2007 and 2018. An AAA diagnosis was made in 4.84% o
f the screening group vs. 1.3% in the non-screening imaging group P<0.001\, yet more aneurysms were found with general imaging studies (50\
,730 vs.15\,449) (Fig 1).
\n
\n
On Kaplan-Meier survival analy
sis\, patients with an AAA diagnosis had higher overall mortality than pat
ients who screened normal\; patients with aneurysms found with non-screeni
ng imaging had the highest mortality\, log-rank P<0.001 (Fig 2).<
/p>\n
\n
The difference in differences regression analysis\, show
ed that the absolute AAA detection rate was 1.55% higher (95% CI 1.2- 1.8)
\, and the mortality was 13.89 % lower (95% CI 10.18 %-16.66 %) after the
introduction of the screening program in 2007.
\n
\n
Multivari
ate Cox regression analysis in patients with AAA diagnosis (65-74-year-old
) demonstrated a significantly lower 5-year mortality [HR 0.45 (95% CI 0.4
3-0.48)] for patients in the US Screening group P<0.001.
\n
\n
Conclusions: In a nationwide a
nalysis of VA patients\, implementation of AAA screening was associated wi
th improved survival and a higher rate of AAA diagnosis. These findings pr
ovide further support for this program’s continuation versus defaulting to
incidental recognition following other abdominal imaging.
\n
\n
ABOUT MANUEL GARCIA-TOCA \nDr. Garcia-Toca earned
his medical degree at the Universidad Anahuac in Mexico 1999. He has a ma
ster’s degree in Health Policy from Stanford University.
\n
\n
He received his general surgery training at the Massachusetts General Hos
pital and Brown University in 2008. He then completed a Vascular Surgery f
ellowship at Northwestern University in 2010. Dr. Garcia-Toca is board cer
tified in both surgery and vascular surgery.
\n
\n
Dr. Garcia-
Toca joined Stanford Vascular Surgery in 2015. He is currently Clinical Pr
ofessor of Surgery in the Division of Vascular Surgery. Dr. Garcia-Toca ha
d previously served as an Assistant Professor of Surgery at Brown Universi
ty. Dr. Garcia Toca is a Staff Surgeon at Santa Clara Valley Medical Cent
er in San Jose.
\n
\n
His research interests include new thera
peutic strategies and outcomes for the management of vascular trauma\, cer
ebrovascular diseases\, dialysis access\, aortic dissection and aneurysms.
\n
\n
ABOUT OLIVER O. AALAMI \nDr. Aala
mi is a Clinical Associate Professor of Vascular & Endovascular Surgery at
Stanford University and the Palo Alto VA and serves as the Lead Director
of Stanford’s Biodesign for Digital Health. He is the course director for
Biodesign for Digital Health\, Building for Digital Health and co-founder
of the open source project\, CardinalKit\, developed to support sensor-b
ased mobile research projects. His primary research focuses on clinically
validating the sensors in smartphones and smartwatches in patients with c
ardiovascular disease to further precision health implementation.
\n
\n
Hosted by: Garry Gold\, M.D. \nSponsored by th
e PHIND Center and the Department of Radiology
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/8616164417003/WN_5z
--vTmvRu6l62kOUd9sZg
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2417@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:Canary Center\,IBIIS\,MIPS\,PHIND\,Radiology\,RSL
CONTACT:Marta Flory\; flory@stanford.edu
DESCRIPTION:Targeted violence continues against Black Americans\, Asian Ame
ricans\, and all people of color. The department of radiology diversity co
mmittee is running a racial equity challenge to raise awareness of systemi
c racism\, implicit bias and related issues. Participants will be provided
a list of resources on these topics such as articles\, podcasts\, videos\
, etc.\, from which they can choose\, with the “challenge” of engaging wit
h one to three media sources prior to our session (some videos are as shor
t as a few minutes). Participants will meet in small-group breakout sessio
ns to discuss what they’ve learned and share ideas.\nPlease reach out to M
arta Flory\, flory@stanford.edu with questions. For details about the sess
ion\, including recommended resources and the Zoom link\, please reach out
to Meke Faaoso at mfaaoso@stanford.edu.\nTickets: https://docs.google.com
/spreadsheets/d/1ehKqHm32peHcm7NQJ427OaKIa9JpfHVunjBk66etZGc/edit?usp=shar
ing.
DTSTART;TZID=America/Los_Angeles:20210430T120000
DTEND;TZID=America/Los_Angeles:20210430T130000
LOCATION:Zoom
SEQUENCE:0
SUMMARY:Racial Equity Challenge: Race in society
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/racial-e
quity-challenge-race-in-society/
X-COST-TYPE:external
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oads/2021/04/shield.png\;225\;225\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
Targeted violence continues against Black Ameri
cans\, Asian Americans\, and all people of color. The department of radiol
ogy diversity committee is running a racial equity challenge to raise awar
eness of systemic racism\, implicit bias and related issues. Participants
will be provided a list of resources on these topics such as articles\, po
dcasts\, videos\, etc.\, from which they can choose\, with the “challenge”
of engaging with one to three media sources prior to our session (some vi
deos are as short as a few minutes). Participants will meet in small-group
breakout sessions to discuss what they’ve learned and share ideas.
\n<
p>Please reach out to Marta Flory\, fl
ory@stanford.edu with questions. For details about the session\, inclu
ding recommended resources and the Zoom link\, please reach out to Meke Fa
aoso at mfaaoso@stanford.edu.\n
X-TICKETS-URL:https://docs.google.com/spreadsheets/d/1ehKqHm32peHcm7NQJ427O
aKIa9JpfHVunjBk66etZGc/edit?usp=sharing
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1757@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:Canary Center\,Early Cancer Detection Seminar Ser
ies
CONTACT:Ashley Williams\; ashleylw@stanford.edu\; https://canarycenter.stan
ford.edu/seminars.html
DESCRIPTION:CEDSS: “Building a Scalable Clinical Genomics Program: How tumo
r\, normal\, and plasma DNA sequencing are informing cancer care\, cancer
risk\, and cancer detection”\n \nMichael Berger\, Ph.D.\nElizabeth and Fel
ix Rohatyn Chair & Associate Director of the Marie-Josée and Henry R. Krav
is Center for Molecular Oncology\nMemorial Sloan Kettering Cancer Center\n
\nZoom Details\nMeeting URL: https://stanford.zoom.us/s/92559505314\nDial
: US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)\nMeeting ID: 925 595
0 5314\nPasscode: 418727\n11:00am – 12:00pm Seminar & Discussion\nRSVP Her
e\n \nABSTRACT\nTumor molecular profiling is a fundamental component of pr
ecision oncology\, enabling the identification of oncogenomic mutations th
at can be targeted therapeutically. To accelerate enrollment to clinical t
rials of molecularly targeted agents and guide treatment selection\, we ha
ve established a center-wide\, prospective clinical sequencing program at
Memorial Sloan Kettering Cancer Center using a custom\, paired tumor-blood
normal sequencing assay (MSK-IMPACT)\, which we have used to profile more
than 50\,000 patients with solid tumors. Yet beyond just the characteriza
tion of tumor-specific alterations\, the inclusion of blood DNA has readil
y enabled the identification of germline risk alleles and somatic mutation
s associated with clonal hematopoiesis. To complement this approach\, we h
ave also implemented a ‘liquid biopsy’ cfDNA panel (MSK-ACCESS) for cancer
detection\, surveillance\, and treatment selection and monitoring. In my
talk\, I will describe the prevalence of somatic and germline genomic alte
rations in a real-world population\, the clinical benefits of cfDNA assess
ment\, and how clonal hematopoiesis can inform cancer risk and confound li
quid biopsy approaches to cancer detection.\n \nABOUT\nMichael Berger\, Ph
D\, holds the Elizabeth and Felix Rohatyn Chair and is Associate Director
of the Marie-Josée and Henry R. Kravis Center for Molecular Oncology at Me
morial Sloan Kettering Cancer Center\, a multidisciplinary initiative to p
romote precision oncology through genomic analysis to guide the diagnosis
and treatment of cancer patients. He is also an Associate Attending Geneti
cist in the Department of Pathology with expertise in cancer genomics\, co
mputational biology\, and high-throughput DNA sequencing technology. His l
aboratory is developing experimental and computational methods to characte
rize the genetic makeup of individual cancers and identify genomic biomark
ers of drug response and resistance. As Scientific Director of Clinical NG
S in the Molecular Diagnostics Service\, he oversees the development and b
ioinformatics associated with clinical sequencing assays\, and he helped l
ead the development and implementation of MSK-IMPACT\, a comprehensive FDA
-authorized tumor sequencing panel that been used to profile more than 60\
,000 tumors from advanced cancer patients at MSK. The resulting data have
enabled the characterization of somatic and germline biomarkers across man
y cancer types and the identification of mutations associated with clonal
hematopoiesis. Dr. Berger also led the development of a clinically validat
ed plasma cell-free DNA assay\, MSK-ACCESS\, which his laboratory is using
to explore tumor evolution\, acquired drug resistance\, and occult metast
atic disease. He received his Bachelor’s Degree in Physics from Princeton
University and his Ph.D. in Biophysics from Harvard University.\n \nHosted
by: Utkan Demirci\, Ph.D.\nSponsored by: The Canary Center & the Departme
nt of Radiology \nStanford University – School of Medicine\nTickets: https
://stanford.zoom.us/webinar/register/5516153318622/WN_MT7TTEciRoWmLVP9GlsJ
RA.
DTSTART;TZID=America/Los_Angeles:20210511T110000
DTEND;TZID=America/Los_Angeles:20210511T120000
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:Cancer Early Detection Seminar Series – Michael Berger\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/cancer-e
arly-detection-seminar-series-michael-f-berger-ph-d/
X-COST-TYPE:external
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
CEDSS: “Building a
Scalable Clinical Genomics Program: How tumor\, normal\, and plasma DNA se
quencing are informing cancer care\, cancer risk\, and cancer detection”
em>
Elizabeth and Fel
ix Rohatyn Chair & Associate Director of the Marie-Josée and Henry R. Krav
is Center for Molecular Oncology \nMemorial Sloan Kettering Cancer Ce
nter
11:00am – 12:00pm Seminar & Discussio
n \nRSVP Here
\n
\n
ABSTRAC
T \nTumor molecular profiling is a fundamental component of
precision oncology\, enabling the identification of oncogenomic mutations
that can be targeted therapeutically. To accelerate enrollment to clinical
trials of molecularly targeted agents and guide treatment selection\, we
have established a center-wide\, prospective clinical sequencing program a
t Memorial Sloan Kettering Cancer Center using a custom\, paired tumor-blo
od normal sequencing assay (MSK-IMPACT)\, which we have used to profile mo
re than 50\,000 patients with solid tumors. Yet beyond just the characteri
zation of tumor-specific alterations\, the inclusion of blood DNA has read
ily enabled the identification of germline risk alleles and somatic mutati
ons associated with clonal hematopoiesis. To complement this approach\, we
have also implemented a ‘liquid biopsy’ cfDNA panel (MSK-ACCESS) for canc
er detection\, surveillance\, and treatment selection and monitoring. In m
y talk\, I will describe the prevalence of somatic and germline genomic al
terations in a real-world population\, the clinical benefits of cfDNA asse
ssment\, and how clonal hematopoiesis can inform cancer risk and confound
liquid biopsy approaches to cancer detection.
\n
\n
AB
OUT \nMichael Berger\, PhD\, holds the Elizabeth and Felix R
ohatyn Chair and is Associate Director of the Marie-Josée and Henry R. Kra
vis Center for Molecular Oncology at Memorial Sloan Kettering Cancer Cente
r\, a multidisciplinary initiative to promote precision oncology through g
enomic analysis to guide the diagnosis and treatment of cancer patients. H
e is also an Associate Attending Geneticist in the Department of Pathology
with expertise in cancer genomics\, computational biology\, and high-thro
ughput DNA sequencing technology. His laboratory is developing experimenta
l and computational methods to characterize the genetic makeup of individu
al cancers and identify genomic biomarkers of drug response and resistance
. As Scientific Director of Clinical NGS in the Molecular Diagnostics Serv
ice\, he oversees the development and bioinformatics associated with clini
cal sequencing assays\, and he helped lead the development and implementat
ion of MSK-IMPACT\, a comprehensive FDA-authorized tumor sequencing panel
that been used to profile more than 60\,000 tumors from advanced cancer pa
tients at MSK. The resulting data have enabled the characterization of som
atic and germline biomarkers across many cancer types and the identificati
on of mutations associated with clonal hematopoiesis. Dr. Berger also led
the development of a clinically validated plasma cell-free DNA assay\, MSK
-ACCESS\, which his laboratory is using to explore tumor evolution\, acqui
red drug resistance\, and occult metastatic disease. He received his Bache
lor’s Degree in Physics from Princeton University and his Ph.D. in Biophys
ics from Harvard University.
\n
\n
Hosted by: Utkan Demirc
i\, Ph.D. \nSponsored by
: The Canary Center & the Department of Radiology \nStanfor
d University – School of Medicine
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/5516153318622/WN_MT
7TTEciRoWmLVP9GlsJRA
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2421@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND Seminar Series: Multi-Cancer Early Detection Screening Te
sts – “Liquid Biopsy Tests” – Are Here – But Will Payers Provide Insurance
Coverage?\n \nPatricia A. Deverka\, MD\, MS\, MBE\nExecutive Director\nDe
verka Consulting\, LLC\n \nKathryn A. Phillips\, PhD\nProfessor of Health
Economics and Health Services Research\nFounding Director\, UCSF Center fo
r Translational and Policy Research on Personalized Medicine (TRANSPERS)\n
\nLocation: Zoom\nWebinar URL: https://stanford.zoom.us/s/99194110894\nDi
al: US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID: 991 9
411 0894\nPasscode: 044958\n11:00am – 12:00pm Seminar & Discussion\nRSVP H
ere\n \nABSTRACT\nThe emergence of Multi-Cancer Early Detection Screening
Tests (MCED) – “liquid biopsy screening tests” – has generated enormous in
terest because they could fundamentally shift how cancer screening is done
. One company is already offering an MCED test for clinical use as a “lab
developed test” (LDT) – and thus addressing the question of “who will pay”
has become urgent. These tests offer potentially transformative screening
and clinical benefits\, but their characteristics present unique challeng
es to payer coverage decision-making and generate concerns about the poten
tially high cost of widespread adoption.\nWe will present our ongoing work
on examining the unique challenges that MCED present for payer coverage d
ecision-making\, drawing on our extensive experience with coverage and rei
mbursement for new technologies. We will focus on identifying the evidence
generation strategies that could be pursued now to inform payer decision-
making so that coverage policies can be developed that are appropriate and
equitable for this ground-breaking technology.\n \nABOUT PATRICIA A. DEVE
RKA\nDr. Deverka is the Executive Director at Deverka Consulting\, LLC whe
re she focuses on helping biotechnology companies and start-ups develop ev
idence to support payer coverage and clinical adoption of innovative techn
ologies. Her most recent projects have focused on breakthrough tests and
drugs focused on population genomic screening\, cancer\, and ultra-rare di
sorders. Prior to starting her consulting practice\, Dr. Deverka has work
ed in the fields of health economics and outcomes research in both non-pro
fit and for-profit settings as a researcher\, educator\, and department he
ad. She has extensive experience with patient-centered outcomes research\,
drug and diagnostic reimbursement planning\, cost- effectiveness analysis
\, and bioethical issues surrounding the use of new technologies. While wo
rking in academia and several non-profit firms\, she has participated in n
umerous NIH-funded studies to evaluate policy barriers to clinical integra
tion of new genomic technologies and has published extensively on strategi
es to promote evidence generation and data sharing. She is a member of the
National Human Genome Research Institute (NHGRI)’s Genomic Medicine Work
Group and serves as a member of NHGRI’s Advisory Council. Deverka has a me
dical degree from the University of Pittsburgh and is board certified in G
eneral Preventive Medicine and Public Health. She also has a master’s deg
ree in bioethics from the University of Pennsylvania and completed a polic
y fellowship at Duke University’s Institute for Genome Sciences and Policy
.\n \nABOUT KATHRYN A. PHILLIPS\nKathryn A. Phillips founded and leads the
UCSF Center for Translational and Policy Research on Personalized Medicin
e (TRANSPERS)\, which focuses on developing objective evidence on how to e
ffectively\, efficiently\, and equitably implement precision/personalized
medicine into health care. Kathryn has published over 150 peer-reviewed ar
ticles in major journals including JAMA\, New England Journal of Medicine\
, Science\, and Health Affairs. She has had continuous funding from NIH as
a PI for over 25 years and was recently awarded a 5-year NIH grant to exa
mine payer coverage and economic value for emerging genomic technologies (
cell-free DNA tests and tests based on polygenic risk scores). Kathryn ser
ves on the editorial boards for Health Affairs\, Value in Health\, JAMA In
ternal Medicine\, Genetics in Medicine\; is a member of the National Acade
my of Medicine Roundtable on Genomics and Precision Health\; and has serve
d on the governing Board of Directors for GenomeCanada and as an advisor t
o the FDA\, CDC\, and the President’s Council of Advisors on Science and T
echnology. She has also served as an advisor to many diagnostics\, sequenc
ing\, and pharmaceutical companies. Kathryn is Chair of the Global Economi
cs and Evaluation of Clinical Sequencing Working Group\, and a member of a
n evidence review committee for the Institute for Clinical and Economic Re
view (ICER). \n \n \nHosted by: Garry Gold\, M.D.\nSponsored by the PHIND
Center and the Department of Radiology\nTickets: https://stanford.zoom.us
/webinar/register/9516200549922/WN_q4_OV6KhRe6MKb_cPEC3GQ.
DTSTART;TZID=America/Los_Angeles:20210518T110000
DTEND;TZID=America/Los_Angeles:20210518T120000
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:PHIND Seminar – Patricia A. Deverka\, MD\, MS\, MBE & Kathryn A. Ph
illips\, PhD
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-patricia-a-deverka-md-ms-mbe-kathryn-a-phillips-phd/
X-COST-TYPE:external
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PHIND Seminar Series: Multi-C
ancer Early Detection Screening Tests – “Liquid Biopsy Tests” – Are Here –
But Will Payers Provide Insurance Coverage?
\n
\n
Patricia A. Deverka\, MD\, MS\, MBE \nExecutive Director\nDeverka Consulting\, LLC
\n
\n
Kathryn A. Philli
ps\, PhD \nProfessor of Health Economics and Health Services
Research \nFounding Director\, UCSF Center for Translational and
Policy Research on Personalized Medicine (TRANSPERS)
11:00am
– 12:00pm Seminar & Discussion \nRSVP Here
\n
\n
ABSTRACT \nThe emergence of Multi-Can
cer Early Detection Screening Tests (MCED) – “liquid biopsy screening test
s” – has generated enormous interest because they could fundamentally shif
t how cancer screening is done. One company is already offering an MCED te
st for clinical use as a “lab developed test” (LDT) – and thus addressing
the question of “who will pay” has become urgent. These tests offer potent
ially transformative screening and clinical benefits\, but their character
istics present unique challenges to payer coverage decision-making and gen
erate concerns about the potentially high cost of widespread adoption.
\n
We will present our ongoing work on examining the unique challenges t
hat MCED present for payer coverage decision-making\, drawing on our exten
sive experience with coverage and reimbursement for new technologies. We w
ill focus on identifying the evidence generation strategies that could be
pursued now to inform payer decision-making so that coverage policies can
be developed that are appropriate and equitable for this ground-breaking t
echnology.
\n
\n
ABOUT PATRICIA A. DEVERKA \nDr. Deverka is the Executive Director at Deverka Consulting\, LLC whe
re she focuses on helping biotechnology companies and start-ups develop ev
idence to support payer coverage and clinical adoption of innovative techn
ologies. Her most recent projects have focused on breakthrough tests and
drugs focused on population genomic screening\, cancer\, and ultra-rare di
sorders. Prior to starting her consulting practice\, Dr. Deverka has work
ed in the fields of health economics and outcomes research in both non-pro
fit and for-profit settings as a researcher\, educator\, and department he
ad. She has extensive experience with patient-centered outcomes research\,
drug and diagnostic reimbursement planning\, cost- effectiveness analysis
\, and bioethical issues surrounding the use of new technologies. While wo
rking in academia and several non-profit firms\, she has participated in n
umerous NIH-funded studies to evaluate policy barriers to clinical integra
tion of new genomic technologies and has published extensively on strategi
es to promote evidence generation and data sharing. She is a member of the
National Human Genome Research Institute (NHGRI)’s Genomic Medicine Work
Group and serves as a member of NHGRI’s Advisory Council. Deverka has a me
dical degree from the University of Pittsburgh and is board certified in G
eneral Preventive Medicine and Public Health. She also has a master’s deg
ree in bioethics from the University of Pennsylvania and completed a polic
y fellowship at Duke University’s Institute for Genome Sciences and Policy
.
\n
\n
ABOUT KATHRYN A. PHILLIPS \nKath
ryn A. Phillips founded and leads the UCSF Center for Translational an
d Policy Research on Personalized Medicine (TRANSPERS)\, which focuse
s on developing objective evidence on how to effectively\, efficiently\, a
nd equitably implement precision/personalized medicine into health care. K
athryn has published over 150 peer-reviewed articles in major journals inc
luding JAMA\, New England Journal of Medicine\, Scie
nce\, and Health Affairs. She has had continuous funding fro
m NIH as a PI for over 25 years and was recently awarded a 5-year NIH gran
t to examine payer coverage and economic value for emerging genomic techno
logies (cell-free DNA tests and tests based on polygenic risk scores). Kat
hryn serves on the editorial boards for Health Affairs\, Valu
e in Health\, JAMA Internal Medicine\, Genetics in Medic
ine\; is a member of the National Academy of Medicine Roundtable on G
enomics and Precision Health\; and has served on the governing Board of Di
rectors for GenomeCanada and as an advisor to the FDA\, CDC\, and the Pres
ident’s Council of Advisors on Science and Technology. She has also served
as an advisor to many diagnostics\, sequencing\, and pharmaceutical compa
nies. Kathryn is Chair of the Global Economics and Evaluation of Clini
cal Sequencing Working Group\, and a member of an evidence review com
mittee for the Institute for Clinical and Economic Review (ICER).
\n
\n
\n
Hosted by: Garry Gold\
, M.D. \nSponsored by the PHIND Center and the Department of
Radiology
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/9516200549922/WN_q4
_OV6KhRe6MKb_cPEC3GQ
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1573@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND Seminar Series: Pervasive Computing With Everyday Devices
To Build & Sustain Resilience & Wellbeing\nPablo E. Paredes\, PhD\nClinic
al Assistant Professor\, Psychiatry and Behavioral Sciences and\, by court
esy\, Epidemiology and Population Health\nStanford University\n \nZoom Web
inar Details\nWebinar URL: https://stanford.zoom.us/s/99098874758\nDial: U
S: +1 650 724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID: 990 9887 4
758\nPasscode: 784858\n11:00am – 12:00pm Seminar & Discussion\n12:00pm – 1
2:15pm Reception\nRSVP Here\n \nABSTRACT\nAs society progresses towards in
creasing pervasive computing levels\, I design and build technology-enable
d solutions to repurpose everyday devices to help people build resilience
and grow wellbeing. I leverage biological and behavioral knowledge to desi
gn systems that balance user needs and health outcomes while mitigating su
rveillance and agency risks. In this talk\, I present my research on effic
acious and engaging sensors and interventions necessary in the population
and public health domains. I share a series of research projects exploring
and validating novel ideas on passive sensors – less dependent on subject
ive surveys or wearables – and subtle interventions that minimize workflo
w disruption. I show the promise of repurposing existing signals from comp
uting peripherals (i.e.\, mouse and trackpad) or cars (steering wheel) int
o “sensorless” sensors and repurposing existing media as just-in-time micr
o-interventions that can work across multiple scenarios and populations. I
discuss how these data could be used in collaboration with domain experts
to study topics as varied as the interaction between stress and productiv
ity in office workers\, burnout prevention among clinical practitioners\,
or the prevention of depression among rural health workers. Finally\, grou
nded in theories from neuroscience and behavioral economics\, I propose th
e evolution of everyday “mundane” devices\, such as chairs\, desks\, cars\
, or even urban lights\, into adaptive and autonomous wellbeing-optimizing
interventions. I close with a discussion of the research needed to system
atically study ethics in pervasive technology for resilience\, and wellbei
ng.\n \nABOUT\nPablo Paredes earned his Ph.D. in Computer Science from the
University of California\, Berkeley\, in 2015 with Prof. John Canny. He i
s currently a Clinical Assistant Professor in the Psychiatry and Behaviora
l Sciences Department and the Epidemiology and Population Health Departmen
t (by courtesy) at the Stanford University School of Medicine. He leads th
e Pervasive Wellbeing Technology Lab\, which houses a diverse group of stu
dents from multiple departments such as computer science\, electrical engi
neering\, mechanical engineering\, anthropology\, neuroscience\, and lingu
istics. Before joining the School of Medicine\, Dr. Paredes was a Postdoct
oral Researcher in the Computer Science Department at Stanford University
with Prof. James Landay. During his Ph.D. career\, he held internships on
behavior change and affective computing at Microsoft Research and Google.
He has been an active associate editor for the Interactive\, Mobile\, Wire
less\, and Ubiquitous Technology Journal (IMWUT) and a reviewer and editor
for multiple top CS and medical journals. Before 2010\, he was a senior s
trategic manager with Intel in Sao Paulo\, Brazil\, a lead product manager
with Telefonica in Quito\, Ecuador\, and an entrepreneur in his native Ec
uador and\, more recently\, in the US. In these roles\, he has had the opp
ortunity to hire and closely evaluate designers\, engineers\, business peo
ple\, and researchers in telecommunications and product development. Durin
g his academic career\, Dr. Paredes has advised close to 40 mentees\, incl
uding postdocs\, Ph.D.\, master’s\, and undergraduate students\, collabora
ted with colleagues from multiple departments across engineering\, medicin
e\, and the humanities\, and raised funding from NSF\, NIH\, and large mul
tidisciplinary intramural research projects.\n \nHosted by: Garry Gold\, M
.D.\nSponsored by the PHIND Center and the Department of Radiology\nTicket
s: https://stanford.zoom.us/webinar/register/8316220421859/WN_thRILWbcQK2h
9DllQXNjRQ.
DTSTART;TZID=America/Los_Angeles:20210615T110000
DTEND;TZID=America/Los_Angeles:20210615T120000
LOCATION:Zoom - See description for more information
SEQUENCE:0
SUMMARY:PHIND Seminar – Pablo E. Paredes\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-pablo-e-paredes-castro-ph-d/
X-COST-TYPE:external
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/wp-content/uploads/2019/10/self_picture_pablo_flowers.jpeg\;2448\;3264\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Seri
es: Pervasive Computing With Everyday Devices To Build & Sustain Resil
ience & Wellbeing
\n
Pablo E. Paredes\, PhD \nClini
cal Assistant Professor\, Psychiatry and Behavioral Sciences and\, by cour
tesy\, Epidemiology and Population Health \nStanford University
ABSTRACT \nAs society progresses towards increasing pervasive computing leve
ls\, I design and build technology-enabled solutions to repurpose everyday
devices to help people build resilience and grow wellbeing. I leverage bi
ological and behavioral knowledge to design systems that balance user need
s and health outcomes while mitigating surveillance and agency risks. In t
his talk\, I present my research on efficacious and engaging sensors and i
nterventions necessary in the population and public health domains. I shar
e a series of research projects exploring and validating novel ideas on pa
ssive sensors – less dependent on subjective surveys or wearables – and s
ubtle interventions that minimize workflow disruption. I show the promise
of repurposing existing signals from computing peripherals (i.e.\, mouse a
nd trackpad) or cars (steering wheel) into “sensorless” sensors and repurp
osing existing media as just-in-time micro-interventions that can work acr
oss multiple scenarios and populations. I discuss how these data could be
used in collaboration with domain experts to study topics as varied as the
interaction between stress and productivity in office workers\, burnout p
revention among clinical practitioners\, or the prevention of depression a
mong rural health workers. Finally\, grounded in theories from neuroscienc
e and behavioral economics\, I propose the evolution of everyday “mundane”
devices\, such as chairs\, desks\, cars\, or even urban lights\, into ada
ptive and autonomous wellbeing-optimizing interventions. I close with a di
scussion of the research needed to systematically study ethics in pervasiv
e technology for resilience\, and wellbeing.
\n
\n
ABO
UT \nPablo Paredes earned his Ph.D. in Computer Science from
the University of California\, Berkeley\, in 2015 with Prof. John Canny.
He is currently a Clinical Assistant Professor in the Psychiatry and Behav
ioral Sciences Department and the Epidemiology and Population Health Depar
tment (by courtesy) at the Stanford University School of Medicine. He lead
s the Pervasive Wellbeing Technology Lab\, which houses a diverse group of
students from multiple departments such as computer science\, electrical
engineering\, mechanical engineering\, anthropology\, neuroscience\, and l
inguistics. Before joining the School of Medicine\, Dr. Paredes was a Post
doctoral Researcher in the Computer Science Department at Stanford Univers
ity with Prof. James Landay. During his Ph.D. career\, he held internships
on behavior change and affective computing at Microsoft Research and Goog
le. He has been an active associate editor for the Interactive\, Mobile\,
Wireless\, and Ubiquitous Technology Journal (IMWUT) and a reviewer and ed
itor for multiple top CS and medical journals. Before 2010\, he was a seni
or strategic manager with Intel in Sao Paulo\, Brazil\, a lead product man
ager with Telefonica in Quito\, Ecuador\, and an entrepreneur in his nativ
e Ecuador and\, more recently\, in the US. In these roles\, he has had the
opportunity to hire and closely evaluate designers\, engineers\, business
people\, and researchers in telecommunications and product development. D
uring his academic career\, Dr. Paredes has advised close to 40 mentees\,
including postdocs\, Ph.D.\, master’s\, and undergraduate students\, colla
borated with colleagues from multiple departments across engineering\, med
icine\, and the humanities\, and raised funding from NSF\, NIH\, and large
multidisciplinary intramural research projects.
\n
\n
Hos
ted by: Garry Gold\, M.D. \nSponsored by the PHIND Center an
d the Department of Radiology
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/8316220421859/WN_th
RILWbcQK2h9DllQXNjRQ
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2725@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND & CDH Seminar: “The Invisible Future of Health Monitoring
”\nJoin Stanford CDH and PHIND on Wednesday\, June 23rd at 3:15 PM PDT to
hear some of the industry’s leading experts talk about embedded sensors\,
longitudinal data collection\, the future of remote monitoring\, and real-
world applications of precision health technologies. The panel will featur
e: Nicolas Genain\, MS\, Withings\; John O Moore MD\, PhD\, Fitbit Health
Solutions at Google\; Pablo Paredes\, PhD\, MBA\, MS\, Stanford University
\; and Michael Synder\, PhD\, Stanford University. The discussion will be
moderated by Jun (Alex) Gao\, MS\, Samsung America.\n \nZoom Webinar Detai
ls\nWebinar URL: https://stanford.zoom.us/s/96984014176\nDial: US: +1 650
724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID: 969 8401 4176\nPassc
ode: 375941\n3:15pm – 4:15pm: Panel Discussion\nRSVP Here\n \n \nSponsored
by the PHIND Center and Center for Digital Health\nTickets: https://stanf
ord.zoom.us/webinar/register/7016228432975/WN_7RpA06gIQICRCH6bzQjt3w.
DTSTART;TZID=America/Los_Angeles:20210623T151500
DTEND;TZID=America/Los_Angeles:20210623T161500
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:“The Invisible Future of Health Monitoring” – PHIND & CDH Seminar
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/the-invi
sible-future-of-health-monitoring-phind-cdh-seminar/
X-COST-TYPE:external
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PH
IND & CDH Seminar: “The Invisible Future of Health Monitoring”
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/7016228432975/WN_7R
pA06gIQICRCH6bzQjt3w
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2803@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI\,IBIIS\,Radiology\,RSL
CONTACT:
DESCRIPTION:Radiology Department-Wide Research Meeting\n• Research Announce
ments\n• Mirabela Rusu\, PhD – Learning MRI Signatures of Aggressive Prost
ate Cancer: Bridging the Gap between Digital Pathologists and Digital Radi
ologists\n• Akshay Chaudhari\, PhD – Data-Efficient Machine Learning for M
edical Imaging\nLocation: Zoom – Details can be found here: https://radres
earch.stanford.edu\nMeetings will be the 3rd Friday of each month.\n \nHos
ted by: Kawin Setsompop\, PhD\nSponsored by: the the Department of Radiolo
gy
DTSTART;TZID=America/Los_Angeles:20210716T120000
DTEND;TZID=America/Los_Angeles:20210716T130000
LOCATION:Zoom – Details can be found here: https://radresearch.stanford.edu
SEQUENCE:0
SUMMARY:Radiology-Wide Research Conference
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/radiolog
y-wide-research-conference/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2021/07/RWRC-July-150x150.jpeg\;150\;150\;1\,m
edium\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content
/uploads/2021/07/RWRC-July-300x195.jpeg\;300\;195\;1\,large\;http://web.st
anford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/RWR
C-July.jpeg\;443\;288\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
\n
Radiology Department-Wide Research Meeting
\n
•
Research Announcements \n• Mirabela Rusu\, PhD – Learning MRI Signat
ures of Aggressive Prostate Cancer: Bridging the Gap between Digital Patho
logists and Digital Radiologists \n• Akshay Chaudhari\, PhD – Data-Ef
ficient Machine Learning for Medical Imaging
Hosted by: Kawin Setsompop\, PhD \nSponsore
d by: the the Department of Radiology
\n
\n\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1595@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu\; https://med.stanford.edu/
phind/events/2021.html
DESCRIPTION:PHIND Seminar Series: Plastic-based sensors for wearable techno
logies: fundamentals and applications\n \nAlberto Salleo\, Ph.D.\nProfesso
r of Material Sciences and Engineering\nStanford University\n \nZoom Webin
ar Details\nWebinar URL: https://stanford.zoom.us/s/92646686705\nDial: US:
+1 650 724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID: 926 4668 670
5\nPasscode: 270341\n11:00am – 12:00pm Seminar & Discussion\nRSVP Here\n
\nABSTRACT\nThe continuous monitoring of human health can greatly benefit
from devices that can be worn comfortably or seamlessly integrated in hous
ehold objects\, constituting “health-centered” domotics. One of the key as
pects for these devices to be successful is to be invisibly integrated and
disappear in the background of our lives. Our group works on thin film de
vices made with plastic materials that can be used for electrochemically s
ensing of common analytes from easily accessible bodily fluids (e.g. sweat
\, saliva\, urine) and can be easily multiplexed. I will describe electroc
hemical transistors that detect ionic species either directly present in b
ody fluids or resulting from a selective enzymatic reaction (e.g. ammonia
from creatinine) at physiological levels. I will also show that non-charge
d molecules can be detected by making use of custom-processed polymer memb
ranes that act as “synthetic enzymes”. Using these membranes in conjunctio
n with electrochemical transistors we demonstrate that we are able to meas
ure physiological levels of cortisol in real human sweat. Importantly\, tr
ansistors can amplify signals and I will show what architectures must be u
sed to observe 1000x amplification of sensing currents.\nFinally we have d
eveloped a process that allows us to fabricate sensor arrays on flexible s
ubstrates thereby opening the door towards ultra-thin\, flexible sensor ar
rays for wearable technologies.\n \nABOUT\nAlberto Salleo is currently Ful
l Professor of Materials Science and Department Chair at Stanford Universi
ty. Alberto Salleo holds a Laurea degree in Chemistry from La Sapienza and
graduated as a Fulbright Fellow with a PhD in Materials Science from UC B
erkeley in 2001. From 2001 to 2005 Salleo was first post-doctoral research
fellow and successively member of research staff at Xerox Palo Alto Resea
rch Center. In 2005 Salleo joined the Materials Science and Engineering De
partment at Stanford as an Assistant Professor in 2006. Salleo is a Princi
pal Editor of MRS Communications since 2011.While at Stanford\, Salleo won
the NSF Career Award\, the 3M Untenured Faculty Award\, the SPIE Early Ca
reer Award\, the Tau Beta Pi Excellence in Undergraduate Teaching Award\,
and the Gores Award for Excellence in Teaching\, Stanford’s highest teachi
ng award. He has been a Thomson Reuters Highly Cited Researcher since 2015
\, recognizing that he ranks in the top 1% cited researchers in his field.
\n \nHosted by: Garry Gold\, M.D.\nSponsored by the PHIND Center and the D
epartment of Radiology\nTickets: https://stanford.zoom.us/webinar/register
/2816249009305/WN_lUezgp98RMKzD7rC6oeRFg.
DTSTART;TZID=America/Los_Angeles:20210720T110000
DTEND;TZID=America/Los_Angeles:20210720T120000
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:PHIND Seminar – Alberto Salleo\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-alberto-salleo-ph-d/
X-COST-TYPE:external
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dar/wp-content/uploads/2019/10/alberto-salleo_profilephoto.jpg\;350\;350\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Seri
es: Plastic-based sensors for wearable technologies: fundamentals and
applications
\n
\n
Alberto Salleo\, Ph.D. \nProfes
sor of Material Sciences and Engineering \nStanford University
ABSTRACT
strong> \nThe continuous monitoring of human health can greatly benef
it from devices that can be worn comfortably or seamlessly integrated in h
ousehold objects\, constituting “health-centered” domotics. One of the key
aspects for these devices to be successful is to be invisibly integrated
and disappear in the background of our lives. Our group works on thin film
devices made with plastic materials that can be used for electrochemicall
y sensing of common analytes from easily accessible bodily fluids (e.g. sw
eat\, saliva\, urine) and can be easily multiplexed. I will describe elect
rochemical transistors that detect ionic species either directly present i
n body fluids or resulting from a selective enzymatic reaction (e.g. ammon
ia from creatinine) at physiological levels. I will also show that non-cha
rged molecules can be detected by making use of custom-processed polymer m
embranes that act as “synthetic enzymes”. Using these membranes in conjunc
tion with electrochemical transistors we demonstrate that we are able to m
easure physiological levels of cortisol in real human sweat. Importantly\,
transistors can amplify signals and I will show what architectures must b
e used to observe 1000x amplification of sensing currents.
\n
Finally
we have developed a process that allows us to fabricate sensor arrays on
flexible substrates thereby opening the door towards ultra-thin\, flexible
sensor arrays for wearable technologies.
\n
\n
ABOUT<
/strong> \nAlberto Salleo is currently Full Professor of Materials Sc
ience and Department Chair at Stanford University. Alberto Salleo holds a
Laurea degree in Chemistry from La Sapienza and graduate
d as a Fulbright Fellow with a PhD in Materials Science from UC Berkeley i
n 2001. From 2001 to 2005 Salleo was first post-doctoral research fellow a
nd successively member of research staff at Xerox Palo Alto Research Cente
r. In 2005 Salleo joined the Materials Science and Engineering Department
at Stanford as an Assistant Professor in 2006. Salleo is a Principal Edito
r of MRS Communications since 2011.While at Stanford\, Salleo won the NSF
Career Award\, the 3M Untenured Faculty Award\, the SPIE Early Career Awar
d\, the Tau Beta Pi Excellence in Undergraduate Teaching Award\, and the G
ores Award for Excellence in Teaching\, Stanford’s highest teaching award.
He has been a Thomson Reuters Highly Cited Researcher since 2015
\, recognizing that he ranks in the top 1% cited researchers in his field.
\n
\n
Hosted by: Garry Gold\, M.D. \nSponso
red by the PHIND Center and the Department of Radiology
BODY>
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/2816249009305/WN_lU
ezgp98RMKzD7rC6oeRFg
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2809@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI\,Annual Conferences
CONTACT:AIMI Center\; aimicenter@stanford.edu\; https://aimi.stanford.edu/n
ews-events/aimi-symposium/overview
DESCRIPTION:Stanford AIMI Director Curt Langlotz and Co-Directors Matt Lung
ren and Nigam Shah invite you to join us on August 3 for the 2021 Stanford
Center for Artificial Intelligence in Medicine and Imaging (AIMI) Symposi
um. The virtual symposium will focus on the latest\, best research on the
role of AI in diagnostic excellence across medicine\, current areas of imp
act\, fairness and societal impact\, and translation and clinical implemen
tation. The program includes talks\, interactive panel discussions\, and b
reakout sessions. Registration is free and open to all.\n \nAlso\, the 2nd
Annual BiOethics\, the Law\, and Data-sharing: AI in Radiology (BOLD-AIR)
Summit will be held on August 4\, in conjunction with the AIMI Symposium.
The summit will convene a broad range of speakers in bioethics\, law\, re
gulation\, industry groups\, and patient safety and data privacy\, to addr
ess the latest ethical\, regulatory\, and legal challenges regarding AI in
radiology.\n \nREGISTER HERE\nTickets: https://www.eventbrite.com/e/2021-
stanford-aimi-symposium-bold-air-summit-registration-152725816027.
DTSTART;TZID=America/Los_Angeles:20210803T080000
DTEND;TZID=America/Los_Angeles:20210804T150000
LOCATION:Virtual Livestream
SEQUENCE:0
SUMMARY:2021 AIMI Symposium + BOLD-AIR Summit
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/2021-aim
i-symposium-bold-air-summit/
X-COST-TYPE:external
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
Stanfo
rd AIMI Director Curt Langlotz and Co-Directors Matt Lungren and Nigam Sha
h invite you to join us on August 3 for
the 2021 Stanford Center for Artificial Intell
igence in Medicine and Imaging (AIMI) Symp
osium. The virtual symposium will focus on the latest\, best research
on the role of AI in diagnostic excellence across medicine\, current areas
of impact\, fairness and societal impact\, and translation and clinical i
mplementation. The program includes talks\, interactive panel discussions\
, and breakout sessions. Registration is free and open to all.
\n
p>\n
Also\, the 2nd Annual BiOethics\, the L
aw\, and Data-sharing: AI in Radiology (BOLD-AIR) Summit will be held
on August 4\, in conjunction with the AI
MI Symposium. The summit will convene a broad range of speakers in bioethi
cs\, law\, regulation\, industry groups\, and patient safety and data priv
acy\, to address the latest ethical\, regulatory\, and legal challenges re
garding AI in radiology.
X-TICKETS-URL:https://www.eventbrite.com/e/2021-stanford-aimi-symposium-bol
d-air-summit-registration-152725816027
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1619@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu\; https://med.stanford.edu/
phind/events/2021.html
DESCRIPTION:PHIND Seminar Series: Peace of mind for those affected by strok
e\nOrestis Vardoulis\, Ph.D.\nCo-Founder & CEO\nZeitMedical\n \nZoom Webin
ar Details\nWebinar URL: https://stanford.zoom.us/s/94427469356\nDial: US:
+1 650 724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID: 944 2746 935
6\nPasscode: 999031\n11:00am – 12:00pm Seminar & Discussion\n12:00pm – 12:
15pm Reception\nRSVP Here\n \nABSTRACT\nThere is a growing population of o
ver 10 million Americans that live with an elevated risk of having a strok
e.\nEach year approximately 1 million Americans survive a stroke or a mini
stroke\, often severely affected by its debilitating effects. A more disab
ling stroke frequently occurs after the seminal events\, leaving patients
and their families scarred for life.\nTIME = BRAIN. Early hospital present
ation is the most critical determinant in good stroke outcomes. However\,
most patients arrive at the hospital often hours after the event\, with le
ss than 10% receiving any form of treatment (thrombolysis / thrombectomy).
\nAs a result\, at risk individuals struggle daily with the fear\, a strok
e might happen during night-time or when they are alone. Unfortunately a s
troke that goes unnoticed for hours\, is most often not treatable due to t
he lack of salvageable tissue.\nTo alleviate that fear\, we are creating a
n AI-powered\, smart-headband that analyzes brain waves to detect the onse
t of an event immediately\, and alert the patient\, caregivers and 911.\nO
ur stroke detection AI has already been shown to detect ischemia during hi
gh-risk surgeries with 90% sensitivity and no false positives.\nWe have re
ceived FDA breakthrough designation for our solution and are currently run
ning a pilot human factors and signal quality study.\nOur vision is to pro
vide peace of mind and optimal brain health for everyone.\n \nABOUT\nOrest
is is the CEO and Co-founder of Zeit Medical\, a telehealth company that o
ffers at home monitoring and alert solutions for patients at risk for stro
ke. Prior to starting Zeit\, Orestis was a Stanford Biodesign Innovation F
ellow where his team developed the initial idea about at-home stroke detec
tion. Orestis trained as a Mechanical Engineer\, at Aristotle University\,
Greece\, earned his PhD in Biotechnology and Bioengineering at EPFL\, Swi
tzerland and conducted cutting edge research in flexible wearable electron
ics with the Bao Group at Stanford Chemical Engineering. He has authored m
ore than twenty publications in prestigious journals and has filed for a v
ariety of patents at the intersection of materials technology and medical
devices. Orestis currently lives in San Francisco\, where he also contribu
tes to the UCSF-Stanford pediatric device consortium as a technology advis
or. He also maintains close ties with the med-tech and health-tech commun
ities in Switzerland and Greece\, contributing to regional Biodesign educa
tional workshops.\n \nHosted by: Garry Gold\, M.D.\nSponsored by the PHIND
Center and the Department of Radiology\nTickets: https://stanford.zoom.us
/webinar/register/3016264733187/WN_i3dqwzHERYOfa7-Y87t7PQ.
DTSTART;TZID=America/Los_Angeles:20210817T110000
DTEND;TZID=America/Los_Angeles:20210817T120000
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:PHIND Seminar – Orestis Vardoulis\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-orestis-vardoulis-ph-d/
X-COST-TYPE:external
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Series: P
eace of mind for those affected by stroke
There is a growing population of ov
er 10 million Americans that live with an elevated risk of having a stroke
.
\n
Each year approximately 1 million Americans survive a stroke or
a ministroke\, often severely affected by its debilitating effects. A more
disabling stroke frequently occurs after the seminal events\, leaving pat
ients and their families scarred for life.
\n
TIME = BRAIN. Early hos
pital presentation is the most critical determinant in good stroke outcome
s. However\, most patients arrive at the hospital often hours after the ev
ent\, with less than 10% receiving any form of treatment (thrombolysis / t
hrombectomy).
\n
As a result\, at risk individuals struggle daily wit
h the fear\, a stroke might happen during night-time or when they are alon
e. Unfortunately a stroke that goes unnoticed for hours\, is most often no
t treatable due to the lack of salvageable tissue.
\n
To alleviate th
at fear\, we are creating an AI-powered\, smart-headband that analyzes bra
in waves to detect the onset of an event immediately\, and alert the patie
nt\, caregivers and 911.
\n
Our stroke detection AI has already been
shown to detect ischemia during high-risk surgeries with 90% sensitivity a
nd no false positives.
\n
We have received FDA breakthrough designati
on for our solution and are currently running a pilot human factors and si
gnal quality study.
\n
Our vision is to provide peace of mind and opt
imal brain health for everyone.
\n
\n
ABOUT\nOrestis is the CEO and Co-founder of Zeit Medical\, a telehealth com
pany that offers at home monitoring and alert solutions for patients at ri
sk for stroke. Prior to starting Zeit\, Orestis was a Stanford Biodesign I
nnovation Fellow where his team developed the initial idea about at-home s
troke detection. Orestis trained as a Mechanical Engineer\, at Aristotle U
niversity\, Greece\, earned his PhD in Biotechnology and Bioengineering at
EPFL\, Switzerland and conducted cutting edge research in flexible wearab
le electronics with the Bao Group at Stanford Chemical Engineering. He has
authored more than twenty publications in prestigious journals and has fi
led for a variety of patents at the intersection of materials technology a
nd medical devices. Orestis currently lives in San Francisco\, where he al
so contributes to the UCSF-Stanford pediatric device consortium as a techn
ology advisor. He also maintains close ties with the med-tech and health-
tech communities in Switzerland and Greece\, contributing to regional Biod
esign educational workshops.
\n
\n
Hosted by: Garry Gold\,
M.D. \nSponsored by the PHIND Center and the Department of
Radiology
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/3016264733187/WN_i3
dqwzHERYOfa7-Y87t7PQ
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1645@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND Seminar Series: Towards precision diagnostic and predicti
on of food allergy\nSindy KY Tang\, Ph.D.\nAssociate Professor of Mechanic
al Engineering\, Senior Fellow at the Woods Institute for the Environment
and Professor\, by courtesy\, of Radiology – PHIND Center\nStanford Univer
sity\n \nLocation: Zoom\nWebinar URL: https://stanford.zoom.us/s/919329663
34\nDial: US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)\nWebinar ID:
919 3296 6334\nPasscode: 383071\n11:00am – 12:00pm Seminar & Discussion\n
RSVP Here\n \nABSTRACT\nFood allergy has reached epidemic proportions. Acc
urate in vitro methods that are efficient and easy to use to identify offe
nding food allergens are lacking. Oral food challenge\, the gold standard
for food allergy assessment\, is often not performed as it places the pati
ent at risk of anaphylaxis. As such\, food allergy is often identified onl
y after an adverse reaction that could be life-threatening. Our long-term
goal is to develop a food allergy diagnostic test that is accurate\, safe\
, rapid\, and accessible\, so that food allergy can be easily identified p
rior to the occurrence of an adverse reaction\, and that the efficacy of i
mmunotherapy for food allergy can be tracked more effectively. This talk w
ill discuss our recent work on developing such a test. Our approach is bas
ed on the Basophil Activation Test (BAT)\, which measures the activation o
f basophils in whole blood after stimulation with specific food allergens
ex vivo. The BAT has been shown to be highly predictive of allergic reacti
ons. However\, the need for flow cytometry has limited its broader use. We
are developing a miniaturized\, standalone version of the BAT. We envisio
n that the test can be used at the point of care\, such as the doctor’s of
fice or at a local pharmacy.\n \nABOUT\nProf. Sindy KY Tang is the Kenneth
and Barbara Oshman Faculty Scholar and Associate Professor of Mechanical
Engineering and by courtesy of Radiology (Precision Health and Integrated
Diagnostics) at Stanford University. She received her Ph.D. from Harvard U
niversity in Engineering Sciences under the supervision of Prof. George Wh
itesides. Her lab at Stanford works on the fundamental understanding of fl
uid mechanics and mass transport in micro-nano systems\, and the applicati
on of this knowledge towards problems in biology\, rapid diagnostics for h
ealth and environmental sustainability. The current areas of focus include
the flow physics of confined micro-droplets using experimental and machin
e learning methods\, interfacial mass transport and self-assembly\, and ul
trahigh throughput opto-microfluidic systems for disease diagnostics\, wat
er and energy sustainability\, and single-cell wound healing studies. She
was a Stanford Biodesign Faculty Fellow in 2018. Dr. Tang’s work has been
recognized by multiple awards including the NSF CAREER Award\, 3M Nontenur
ed Faculty Award\, the ACS Petroleum Fund New Investigator Award\, and inv
ited lecture at the Nobel Symposium on Microfluidics in Sweden. Website: h
ttp://web.stanford.edu/group/tanglab/\n \nHosted by: Garry Gold\, M.D.\nSp
onsored by the PHIND Center and the Department of Radiology\nTickets: http
s://stanford.zoom.us/webinar/register/1216286302579/WN_3iFMsumAT9iKlV5G1Vr
9zA.
DTSTART;TZID=America/Los_Angeles:20210921T110000
DTEND;TZID=America/Los_Angeles:20210921T120000
LOCATION:Zoom - See Description for Zoom Link
SEQUENCE:0
SUMMARY:PHIND Seminar – Sindy KY Tang\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-sindy-ky-tang-ph-d/
X-COST-TYPE:external
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Series:
Towards precision diagnostic and prediction of food allergy
\n
Sindy KY Tan
g\, Ph.D. \nAssociate Professor of Mechanical Engineerin
g\, Senior Fellow at the Woods Institute for the Environment and Professor
\, by courtesy\, of Radiology – PHIND Center \nStanford University
11:00am – 12:00pm Seminar & Discussion
\nRSVP Here
\n
\n
ABSTRACT
\nFood allergy has reached epidemic proportions. Accurate i
n vitro methods that are efficient and easy to use to identify offending f
ood allergens are lacking. Oral food challenge\, the gold standard for foo
d allergy assessment\, is often not performed as it places the patient at
risk of anaphylaxis. As such\, food allergy is often identified only after
an adverse reaction that could be life-threatening. Our long-term goal is
to develop a food allergy diagnostic test that is accurate\, safe\, rapid
\, and accessible\, so that food allergy can be easily identified prior to
the occurrence of an adverse reaction\, and that the efficacy of immunoth
erapy for food allergy can be tracked more effectively. This talk will dis
cuss our recent work on developing such a test. Our approach is based on t
he Basophil Activation Test (BAT)\, which measures the activation of basop
hils in whole blood after stimulation with specific food allergens ex vivo
. The BAT has been shown to be highly predictive of allergic reactions. Ho
wever\, the need for flow cytometry has limited its broader use. We are de
veloping a miniaturized\, standalone version of the BAT. We envision that
the test can be used at the point of care\, such as the doctor’s office or
at a local pharmacy.
\n
\n
ABOUT \nProf
. Sindy KY Tang is the Kenneth and Barbara Oshman Faculty Scholar and Asso
ciate Professor of Mechanical Engineering and by courtesy of Radiology (Pr
ecision Health and Integrated Diagnostics) at Stanford University. She rec
eived her Ph.D. from Harvard University in Engineering Sciences under the
supervision of Prof. George Whitesides. Her lab at Stanford works on the f
undamental understanding of fluid mechanics and mass transport in micro-na
no systems\, and the application of this knowledge towards problems in bio
logy\, rapid diagnostics for health and environmental sustainability. The
current areas of focus include the flow physics of confined micro-droplets
using experimental and machine learning methods\, interfacial mass transp
ort and self-assembly\, and ultrahigh throughput opto-microfluidic systems
for disease diagnostics\, water and energy sustainability\, and single-ce
ll wound healing studies. She was a Stanford Biodesign Faculty Fellow in 2
018. Dr. Tang’s work has been recognized by multiple awards including the
NSF CAREER Award\, 3M Nontenured Faculty Award\, the ACS Petroleum Fund Ne
w Investigator Award\, and invited lecture at the Nobel Symposium on Micro
fluidics in Sweden. Website: http://web.stanford.edu/group/tanglab/
\n
\n
Hoste
d by: Garry Gold\, M.D. \nSponsored by the PHIND Center and
the Department of Radiology
X-TICKETS-URL:https://stanford.zoom.us/webinar/register/1216286302579/WN_3i
FMsumAT9iKlV5G1Vr9zA
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2989@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; http://ibiis.stan
ford.edu/events/seminars/2021seminars.html
DESCRIPTION: \n\nRegina Barzilay\, PhD\nSchool of Engineering Distinguished
Professor for AI and Health\nElectrical Engineering and Computer Science
Department\nAI Faculty Lead at Jameel Clinic for Machine Learning in Healt
h\nComputer Science and Artificial Intelligence Lab\nMassachusetts Institu
te of Technology\nAbstract:\nIn this talk\, I will present methods for fut
ure cancer risk from medical images. The discussion will explore alternati
ve ways to formulate the risk assessment task and focus on algorithmic iss
ues in developing such models. I will also discuss our experience in trans
lating these algorithms into clinical practice in hospitals around the wor
ld.
DTSTART;TZID=America/Los_Angeles:20210922T110000
DTEND;TZID=America/Los_Angeles:20210922T120000
LOCATION:Zoom: https://stanford.zoom.us/j/99474772502?pwd=NEQrQUQ0MzdtRjFiY
U42TCs2bFZsUT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Seeing the Future from Images: ML-Based Model
s for Cancer Risk Assessment
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-seeing-the-future-from-images-ml-based-models-for-cancer-risk-a
ssessment/
X-COST-TYPE:free
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calendar/wp-content/uploads/2021/08/regina-300x300.jpeg\;300\;300\,medium\
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ds/2021/08/regina-300x300.jpeg\;300\;300\,large\;http://web.stanford.edu/g
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
\n
p>\n
Regina Barzilay\, PhD \nSchool of Engineering Distingui
shed Professor for AI and Health \nElectrical Engineering and Compute
r Science Department \nAI Faculty Lead at Jameel Clinic for Machine L
earning in Health \nComputer Science and Artificial Intelligence Lab<
br />\nMassachusetts Institute of Technology
\n
Abstract: \nIn this talk\, I will present methods for future cancer risk
from medical images. The discussion will explore alternative ways to formu
late the risk assessment task and focus on algorithmic issues in developin
g such models. I will also discuss our experience in translating these alg
orithms into clinical practice in hospitals around the world.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2993@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/retreat/2021Hybrid.html
DESCRIPTION:Keynote:\nSelf-Supervision for Learning from the Bottom Up\nWhy
do self-supervised learning? A common answer is: “because data labeling i
s expensive.” In this talk\, I will argue that there are other\, perhaps m
ore fundamental reasons for working on self-supervision. First\, it should
allow us to get away from the tyranny of top-down semantic categorization
and force meaningful associations to emerge naturally from the raw sensor
data in a bottom-up fashion. Second\, it should allow us to ditch fixed d
atasets and enable continuous\, online learning\, which is a much more nat
ural setting for real-world agents. Third\, and most intriguingly\, there
is hope that it might be possible to force a self-supervised task curricul
um to emerge from first principles\, even in the absence of a pre-defined
downstream task or goal\, similar to evolution. In this talk\, I will touc
h upon these themes to argue that\, far from running its course\, research
in self-supervised learning is only just beginning.
DTSTART;TZID=America/Los_Angeles:20210927T130000
DTEND;TZID=America/Los_Angeles:20210927T163000
LOCATION:https://ibiis.stanford.edu/events/retreat/2021Hybrid.html
SEQUENCE:0
SUMMARY:2021 IBIIS & AIMI Virtual Retreat
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/2021-ibi
is-aimi-virtual-retreat/
X-COST-TYPE:free
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
Keynote:
\n
Self-Supervision for Learning from the Bottom Up
\n
Why do sel
f-supervised learning? A common answer is: “because data labeling is expen
sive.” In this talk\, I will argue that there are other\, perhaps more fun
damental reasons for working on self-supervision. First\, it should allow
us to get away from the tyranny of top-down semantic categorization and fo
rce meaningful associations to emerge naturally from the raw sensor data i
n a bottom-up fashion. Second\, it should allow us to ditch fixed datasets
and enable continuous\, online learning\, which is a much more natural se
tting for real-world agents. Third\, and most intriguingly\, there is hope
that it might be possible to force a self-supervised task curriculum to e
merge from first principles\, even in the absence of a pre-defined downstr
eam task or goal\, similar to evolution. In this talk\, I will touch upon
these themes to argue that\, far from running its course\, research in sel
f-supervised learning is only just beginning.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-2295@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:Canary Center\,Early Cancer Detection Seminar Ser
ies
CONTACT:Ashley Williams\; ashleylw@stanford.edu\; https://canarycenter.stan
ford.edu/seminars.html
DESCRIPTION:CEDSS: The First Cell: A new model for cancer research and trea
tment\nAzra Raza\, M.D.\nChan Soon-Shiong Professor of Medicine\nDirector\
, Myelodysplastic Syndrome Center\nColumbia University Medical Center\n \n
Location: Zoom\nMeeting URL: https://stanford.zoom.us/s/99340345860\nDial:
US: +1 650 724 9799 or +1 833 302 1536 (Toll Free)\nMeeting ID: 993 4034
5860\nPasscode: 711508\nRSVP Here\n \nABSTRACT\nCancer research continues
to be predicated on a 1970’s model of research and treatment. Despite hal
f a century of intense research\, we are failing spectacularly to improve
the outcome for patients with advanced disease. Those who are cured contin
ue to be treated mostly with the older strategies (surgery-chemo-radiation
). Our contention is that the real solution to the cancer problem is to di
agnose cancer early\, at the stage of The First Cell. The rapidly evolving
technologies are doing much in this area but need to be expanded. We stud
y a pre-leukemic condition called myelodysplastic syndrome (MDS) with the
hope that we can detect the first leukemia cells as the disease transforms
to acute myeloid leukemia (AML). Towards this end\, we have collected blo
od and bone marrow samples on MDS and AML patients since 1984. Today\, our
Tissue Repository has more than 60\,000 samples. We propose novel methods
to identify surrogate markers that can identify the First Cell through st
udying the serial samples of patients who evolve from MDS to AML.\n \nABOU
T\nDr. Raza is a Professor of Medicine and Director of the MDS Center at C
olumbia University in New York\, NY.She started her research in Myelodispl
astic Syndromes (MDS) in 1982 and moved to Rush University\, Chicago\, Ill
inois in 1992\, where she was the Charles Arthur Weaver Professor in Oncol
ogy and Director\, Division of Myeloid Diseases. The MDS Program\, along w
ith a Tissue Repository containing more than 50\,000 samples from MDS and
acute leukemia patients was successfully relocated to the University of Ma
ssachusetts in 2004 and to Columbia University in 2010.\nBefore moving to
New York\, Dr. Raza was the Chief of Hematology Oncology and the Gladys Sm
ith Martin Professor of Oncology at the University of Massachussetts in Wo
rcester. She has published the results of her laboratory research and clin
ical trials in prestigious\, peer reviewed journals such as The New Englan
d Journal of Medicine\, Nature\, Blood\, Cancer\, Cancer Research\, Britis
h Journal of Hematology\, Leukemia\, and Leukemia Research. Dr. Raza serve
s on numerous national and international panels as a reviewer\, consultant
and advisor and is the recipient of a number of awards.\n \nHosted by: Ut
kan Demirci\, Ph.D.\nSponsored by: The Canary Center & the Department of R
adiology \nStanford University – School of Medicine
DTSTART;TZID=America/Los_Angeles:20211012T110000
DTEND;TZID=America/Los_Angeles:20211012T120000
LOCATION:Venue coming soon!
SEQUENCE:0
SUMMARY:Cancer Early Detection Seminar Series – Azra Raza\, MD
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/cancer-e
arly-detection-seminar-series-azra-raza-md/
X-COST-TYPE:free
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du/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/10/A_Raza.png\
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r/wp-content/uploads/2019/10/A_Raza.png\;700\;466\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
CEDSS: The First Cell: A new model
for cancer research and treatment
\n
Azra Raza\, M.D. \nChan
Soon-Shiong Professor of Medicine \nDirector\, Myelodysplastic Syndro
me Center \nColumbia University Medical Center
\n
\n
L
ocation: Zoom \nMeeting URL: https:
//stanford.zoom.us/s/99340345860 \nDial: US: +1 650 724 9799 or
+1 833 302 1536 (Toll Free) \nMeeting ID: 993 4034 5860 \nPassco
de: 711508
Cancer research continues to
be predicated on a 1970’s model of research and treatment. Despite half a
century of intense research\, we are failing spectacularly to improve the
outcome for patients with advanced disease. Those who are cured continue
to be treated mostly with the older strategies (surgery-chemo-radiation).
Our contention is that the real solution to the cancer problem is to diagn
ose cancer early\, at the stage of The First Cell. The rapidly evolving te
chnologies are doing much in this area but need to be expanded. We study a
pre-leukemic condition called myelodysplastic syndrome (MDS) with the hop
e that we can detect the first leukemia cells as the disease transforms to
acute myeloid leukemia (AML). Towards this end\, we have collected blood
and bone marrow samples on MDS and AML patients since 1984. Today\, our Ti
ssue Repository has more than 60\,000 samples. We propose novel methods to
identify surrogate markers that can identify the First Cell through study
ing the serial samples of patients who evolve from MDS to AML.
\n
p>\n
ABOUT
\n
Dr. Raza is a Professor of Medicine
and Director of the MDS Center at Columbia University in New York\, NY.She
started her research in Myelodisplastic Syndromes (MDS) in 1982 and moved
to Rush University\, Chicago\, Illinois in 1992\, where she was the Charl
es Arthur Weaver Professor in Oncology and Director\, Division of Myeloid
Diseases. The MDS Program\, along with a Tissue Repository containing more
than 50\,000 samples from MDS and acute leukemia patients was successfull
y relocated to the University of Massachusetts in 2004 and to Columbia Uni
versity in 2010.
\n
Before moving to New York\, Dr. Raza was the Chie
f of Hematology Oncology and the Gladys Smith Martin Professor of Oncology
at the University of Massachussetts in Worcester. She has published the r
esults of her laboratory research and clinical trials in prestigious\, pee
r reviewed journals such as The New England Journal of Medicine\, Nature\,
Blood\, Cancer\, Cancer Research\, British Journal of Hematology\, Leukem
ia\, and Leukemia Research. Dr. Raza serves on numerous national and inter
national panels as a reviewer\, consultant and advisor and is the recipien
t of a number of awards.
\n
\n
Hosted by: Utkan Demirci\,
Ph.D. \nSponsored by: Th
e Canary Center & the Department of Radiology \nStanford Un
iversity – School of Medicine
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1673@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND Seminar Series: Topic TBA\nChristina Curtis\, Ph.D.\nAsso
ciate Professor of Medicine (Oncology) and of Genetics\nStanford Universit
y\n \nLocation: Venue coming soon!\n11:00am – 12:00pm Seminar & Discussion
\n12:00pm – 12:15pm Reception\nRSVP coming soon!\n \nABSTRACT\nComing soon
!\n \nABOUT\nComing soon!\n \nHosted by: Garry Gold\, M.D.\nSponsored by t
he PHIND Center and the Department of Radiology
DTSTART;TZID=America/Los_Angeles:20211019T110000
DTEND;TZID=America/Los_Angeles:20211019T120000
LOCATION:Venue coming soon!
SEQUENCE:0
SUMMARY:PHIND Seminar – Christina Curtis\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-christina-curtis-ph-d/
X-COST-TYPE:free
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large\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content
/uploads/2019/10/image.img_.320.high_.jpg\;320\;410\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Series: Top
ic TBA
\n
Christina Curtis\, Ph.D. \nAssociate Professor of
Medicine (Oncology) and of Genetics \nStanford University
Hosted
by: Garry Gold\, M.D. \nSponsored by the PHIND Center and th
e Department of Radiology
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-1703@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:PHIND\,PHIND Seminar Series
CONTACT:Ashley Williams\; ashleylw@stanford.edu
DESCRIPTION:PHIND Seminar Series: Male Infertility and the Future Risk of V
ascular and CV Disease\nMichael Eisenberg\, M.D.\nAssociate Professor of U
rology and\, by courtesy\, of Obstetrics and Gynecology\nStanford Universi
ty Medical Center\n \nGary M. Shaw\, Ph.D.\nNICU Nurses Professor and Prof
essor\, by courtesy\, of Health Research and Policy (Epidemiology) and of
Obstetrics and Gynecology (Maternal Fetal Medicine)\nStanford University\n
\nLocation: Venue coming soon!\n11:00am – 12:00pm Seminar & Discussion\n1
2:00pm – 12:15pm Reception\nRSVP coming soon!\n \nABSTRACT\nComing soon!\n
\nABOUT\nComing soon!\n \nHosted by: Garry Gold\, M.D.\nSponsored by the
PHIND Center and the Department of Radiology
DTSTART;TZID=America/Los_Angeles:20211116T110000
DTEND;TZID=America/Los_Angeles:20211116T120000
LOCATION:Venue coming soon!
SEQUENCE:0
SUMMARY:PHIND Seminar – Michael Eisenberg\, M.D. & Gary M. Shaw\, Ph.D.
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/phind-se
minar-michael-eisenberg-m-d-gary-m-shaw-ph-d/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2019/10/2020.11_SpeakerMashUp-01-150x150.png\;
150\;150\;1\,medium\;http://web.stanford.edu/group/radweb/cgi-bin/radcalen
dar/wp-content/uploads/2019/10/2020.11_SpeakerMashUp-01-300x150.png\;300\;
150\;1\,large\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp
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1\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-conte
nt/uploads/2019/10/2020.11_SpeakerMashUp-01.png\;1050\;526\;
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
PHIND Seminar Series:
Male Infertility and the Future Risk of Vascular and CV Disease
\n
<
a href='https://profiles.stanford.edu/michael-eisenberg'>Michael E
isenberg\, M.D. \nAssociate Professor of Urology and\, b
y courtesy\, of Obstetrics and Gynecology \nStanford University Medic
al Center
\n
\n
Gary M. Shaw\, Ph.D. \nNICU Nurses Professor
and Professor\, by courtesy\, of Health Research and Policy (Epidemiology
) and of Obstetrics and Gynecology (Maternal Fetal Medicine) \nStanfo
rd University
Hosted by: Garry Gold\, M.D. \nSponsored
by the PHIND Center and the Department of Radiology
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3033@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; http://ibiis.stan
ford.edu/events/seminars/2021seminars.html
DESCRIPTION:Saeed Hassanpour\, PhD\nAssociate Professor of Biomedical Data
Science\nAssociate Professor of Epidemiology\nAssociate Professor of Compu
ter Science\nDartmouth Geisel School of Medicine\nDeep Learning for Histol
ogy Images Analysis\nAbstract:\nWith the recent expansions of whole-slide
digital scanning\, archiving\, and high-throughput tissue banks\, the fiel
d of digital pathology is primed to benefit significantly from deep learni
ng technology. This talk will cover several applications of deep learning
for characterizing histopathological patterns on high-resolution microscop
y images for cancerous and precancerous lesions. Furthermore\, the current
challenges for building deep learning models for pathology image analysis
will be discussed and new methodological advances to address these bottle
necks will be presented.\nAbout:\nDr. Saeed Hassanpour is an Associate Pro
fessor in the Departments of Biomedical Data Science\, Computer Science\,
and Epidemiology at Dartmouth College. His research is focused on machine
learning and multimodal data analysis for precision health. Dr. Hassanpour
has led multiple NIH-funded research projects\, which resulted in novel m
achine learning and deep learning models for medical image analysis and cl
inical text mining to improve diagnosis\, prognosis\, and personalized the
rapies. Before joining Dartmouth\, he worked as a Research Engineer at Mic
rosoft. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a
minor in Biomedical Informatics from Stanford University and completed hi
s postdoctoral training at Stanford Center for Artificial Intelligence in
Medicine & Imaging.
DTSTART;TZID=America/Los_Angeles:20211117T120000
DTEND;TZID=America/Los_Angeles:20211117T130000
LOCATION:Zoom: https://stanford.zoom.us/j/91788140120?pwd=K2NvMHZ2SUFVWjc1d
2xJUndjTG9lQT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Deep Learning for Histology Images Analysis
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-deep-learning-for-histology-images-analysis/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2021/11/Saeed.jpg\;300\;300\,medium\;http://we
b.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/11
/Saeed.jpg\;300\;300\,large\;http://web.stanford.edu/group/radweb/cgi-bin/
radcalendar/wp-content/uploads/2021/11/Saeed.jpg\;300\;300\,full\;http://w
eb.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/1
1/Saeed.jpg\;300\;300
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
S
aeed Hassanpour\, PhD \nAssociate Professor of Biomedical Da
ta Science \nAssociate Professor of Epidemiology \nAssociate Pro
fessor of Computer Science \nDartmouth Geisel School of Medicine
\n
Deep Learning for Histology Images Analysis
\n
Abstract: \nWith the recent expansions of whole-slid
e digital scanning\, archiving\, and high-throughput tissue banks\, the fi
eld of digital pathology is primed to benefit significantly from deep lear
ning technology. This talk will cover several applications of deep learnin
g for characterizing histopathological patterns on high-resolution microsc
opy images for cancerous and precancerous lesions. Furthermore\, the curre
nt challenges for building deep learning models for pathology image analys
is will be discussed and new methodological advances to address these bott
lenecks will be presented.
\n
About:
\n
Dr. Sae
ed Hassanpour is an Associate Professor in the Departments of Biomedical D
ata Science\, Computer Science\, and Epidemiology at Dartmouth College. Hi
s research is focused on machine learning and multimodal data analysis for
precision health. Dr. Hassanpour has led multiple NIH-funded research pro
jects\, which resulted in novel machine learning and deep learning models
for medical image analysis and clinical text mining to improve diagnosis\,
prognosis\, and personalized therapies. Before joining Dartmouth\, he wor
ked as a Research Engineer at Microsoft. Dr. Hassanpour received his Ph.D.
in Electrical Engineering with a minor in Biomedical Informatics from Sta
nford University and completed his postdoctoral training at Stanford Cente
r for Artificial Intelligence in Medicine & Imaging.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3039@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; http://ibiis.stan
ford.edu/events/seminars/2021seminars.html
DESCRIPTION:Indrani Bhattacharya\, PhD\nPostdoctoral Research Fellow\nDepar
tment of Radiology\nStanford University\nTitle: Multimodal Data Fusion for
Selective Identification of Aggressive and Indolent Prostate Cancer on Ma
gnetic Resonance Imaging\nAbstract: Automated methods for detecting prosta
te cancer and distinguishing indolent from aggressive disease on Magnetic
Resonance Imaging (MRI) could assist in early diagnosis and treatment plan
ning. Existing automated methods of prostate cancer detection mostly rely
on ground truth labels with limited accuracy\, ignore disease pathology ch
aracteristics observed on resected tissue\, and cannot selectively identif
y aggressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers
when they co-exist in mixed lesions. This talk will cover multimodal and m
ulti-scale fusion approaches to integrate radiology images\, pathology ima
ges\, and clinical domain knowledge about prostate cancer distribution to
selectively identify and localize aggressive and indolent cancers on prost
ate MRI.\n\nRogier van der Sluijs\, PhD\nPostdoctoral Research Fellow\nDep
artment of Radiology\nStanford University\nTitle: Pretraining Neural Netwo
rks for Medical AI\nAbstract: Transfer learning has quickly become standar
d practice for deep learning on medical images. Typically\, practitioners
repurpose existing neural networks and their corresponding weights to boot
strap model development. This talk will cover several methods to pretrain
neural networks for medical tasks. The current challenges for pretraining
neural networks in Radiology will be discussed and recent advancements tha
t address these bottlenecks will be highlighted.
DTSTART;TZID=America/Los_Angeles:20211215T120000
DTEND;TZID=America/Los_Angeles:20211215T130000
LOCATION:Zoom: https://stanford.zoom.us/j/95371438521?pwd=Y3BheHpUanpESnh6V
UkycVhlUWtodz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Indrani Bhattacharya\, PhD & Rogier van der S
luijs\, PhD
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-indrani-bhattacharya-phd-rogier-van-der-sluijs-phd/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2021/12/Indrani.jpg\;200\;200\,medium\;http://
web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/
12/Indrani.jpg\;200\;200\,large\;http://web.stanford.edu/group/radweb/cgi-
bin/radcalendar/wp-content/uploads/2021/12/Indrani.jpg\;200\;200\,full\;ht
tp://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/
2021/12/Indrani.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Indrani Bhattacharya\, PhD \nPostdoctoral Research Fell
ow \nDepartment of Radiology \nStanford University
\n
Title: Multimodal Data Fusion for Selective Identification of
Aggressive and Indolent Prostate Cancer on Magnetic Resonance Imaging
\n
Abstract: Automated methods for detecting prostate c
ancer and distinguishing indolent from aggressive disease on Magnetic Reso
nance Imaging (MRI) could assist in early diagnosis and treatment planning
. Existing automated methods of prostate cancer detection mostly rely on g
round truth labels with limited accuracy\, ignore disease pathology charac
teristics observed on resected tissue\, and cannot selectively identify ag
gressive (Gleason Pattern≥4) and indolent (Gleason Pattern=3) cancers when
they co-exist in mixed lesions. This talk will cover multimodal and multi
-scale fusion approaches to integrate radiology images\, pathology images\
, and clinical domain knowledge about prostate cancer distribution to sele
ctively identify and localize aggressive and indolent cancers on prostate
MRI.
\n\n
R
ogier van der Sluijs\, PhD \nPostdoctoral Research Fellow \nDepartment of Radiology \nStanford University
\n
Ti
tle: Pretraining Neural Networks for Medical AI
\n
A
bstract: Transfer learning has quickly become standard practice f
or deep learning on medical images. Typically\, practitioners repurpose ex
isting neural networks and their corresponding weights to bootstrap model
development. This talk will cover several methods to pretrain neural netwo
rks for medical tasks. The current challenges for pretraining neural netwo
rks in Radiology will be discussed and recent advancements that address th
ese bottlenecks will be highlighted.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3047@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:Nina Kottler\, MD\, MS\nAssociate Chief Medical Officer\, Clini
cal AI\nVP Clinical Operations\nRadiology Partners\nAbstract:\nWe have a c
all to action in healthcare – we need to drive value. Artificial intellig
ence (AI)\, if deployed correctly\, can help accomplish this lofty mission
. In this discussion we will review the following lessons learned in depl
oying radiology AI at scale: 4 unexpected benefits of implementing AI eme
rgent finding triage\; the importance of investing in AI radiologist educa
tion\; how “most” AI needs to be incorporated into the radiologist workflo
w\; why a platform is required to deploy AI at scale and what a modern pla
tform looks like\; how to use AI to add value to your data\; and\, as Dr.
Curt Langlotz famously said\, why rads (practices) who use AI will replace
those who don’t (a depiction of what the role of the radiologist might lo
ok like in a tech enabled future).\nBio:\nDr. Kottler has been a practicin
g radiologist specializing in emergency imaging for over 16 years. Combin
ing her clinical experience with a graduate degree in applied mathematics\
, she has been using technological innovation to drive value in radiology.
As the first radiologist to join Radiology Partners\, Dr. Kottler has he
ld multiple leadership positions within her practice and is currently the
associate Chief Medical Officer for Clinical AI. Externally Dr. Kottler s
erves on multiple committees for the ACR\, RSNA\, and SIIM. Dr. Kottler i
s also passionate about promoting diversity and creating a culture of belo
nging. As such she is a member of the AAWR\, is a member of the diversity
and inclusion committee at SIIM\, serves on the steering committee for RA
D=\, and leads the education and development division of the Belonging Com
mittee within Radiology Partners.
DTSTART;TZID=America/Los_Angeles:20220119T120000
DTEND;TZID=America/Los_Angeles:20220119T130000
LOCATION:Zoom: https://stanford.zoom.us/j/92632628279?pwd=S3RFdXdEUmEweTNKe
lhrcmVxQUExdz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: AI In Clinical Use – Lessons Learned
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-ai-in-clinical-use-lessons-learned/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/01/Nina-Kottler-2021.jpg\;200\;200\,mediu
m\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/upl
oads/2022/01/Nina-Kottler-2021.jpg\;200\;200\,large\;http://web.stanford.e
du/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/01/Nina-Kottle
r-2021.jpg\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/r
adcalendar/wp-content/uploads/2022/01/Nina-Kottler-2021.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Nina Kottler\, MD\, MS \nAssociate
Chief Medical Officer\, Clinical AI \nVP Clinical Operations \nR
adiology Partners
\n
Abstract: \nWe have a call
to action in healthcare – we need to drive value. Artificial intelligenc
e (AI)\, if deployed correctly\, can help accomplish this lofty mission.
In this discussion we will review the following lessons learned in deployi
ng radiology AI at scale: 4 unexpected benefits of implementing AI emerge
nt finding triage\; the importance of investing in AI radiologist educatio
n\; how “most” AI needs to be incorporated into the radiologist workflow\;
why a platform is required to deploy AI at scale and what a modern platfo
rm looks like\; how to use AI to add value to your data\; and\, as Dr. Cur
t Langlotz famously said\, why rads (practices) who use AI will replace th
ose who don’t (a depiction of what the role of the radiologist might look
like in a tech enabled future).
\n
Bio: \nDr. K
ottler has been a practicing radiologist specializing in emergency imaging
for over 16 years. Combining her clinical experience with a graduate deg
ree in applied mathematics\, she has been using technological innovation t
o drive value in radiology. As the first radiologist to join Radiology Pa
rtners\, Dr. Kottler has held multiple leadership positions within her pra
ctice and is currently the associate Chief Medical Officer for Clinical AI
. Externally Dr. Kottler serves on multiple committees for the ACR\, RSNA
\, and SIIM. Dr. Kottler is also passionate about promoting diversity and
creating a culture of belonging. As such she is a member of the AAWR\, i
s a member of the diversity and inclusion committee at SIIM\, serves on th
e steering committee for RAD=\, and leads the education and development di
vision of the Belonging Committee within Radiology Partners.
\n<
/HTML>
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3053@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:Spyridon (Spyros) Bakas\, PhD\nAssistant Professor in the Depar
tment of Pathology\,\nLaboratory Medicine\, and of Radiology\nCenter for B
iomedical Image Computing and Analytics (CBICA)\nPerelman School of Medici
ne\nUniversity of Pennsylvania\nTitle: Imaging Analytics for Neuro-Oncolog
y:\nTowards Computational Diagnostics\nAbstract: Central nervous system (C
NS) tumors come with vastly heterogeneous histologic\, molecular\, and rad
iographic landscapes\, rendering their precise characterization challengin
g. The rapidly growing fields of biophysical modeling and radiomics have s
hown promise in better characterizing the molecular\, spatial\, and tempor
al heterogeneity of tumors. Integrative analysis of CNS tumors\, including
clinically acquired multi-parametric magnetic resonance imaging (mpMRI)\,
assists in identifying macroscopic quantifiable tumor patterns of invasio
n and proliferation\, potentially leading to improved (a) detection/segmen
tation of tumor subregions and (b) computer-aided diagnostic/prognostic/pr
edictive modeling. This talk will touch upon example studies on this space
\, as well as an overview of the largest to-date real-world federated lear
ning study to detect brain tumor boundaries.
DTSTART;TZID=America/Los_Angeles:20220216T120000
DTEND;TZID=America/Los_Angeles:20220216T130000
LOCATION:ZOOM: https://stanford.zoom.us/j/98789338790?pwd=OXRORjhYUUdaRGJpU
HJZdzZ5NGw5dz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Imaging Analytics for Neuro-Oncology: Towards
Computational Diagnostics
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-imaging-analytics-for-neuro-oncology-towards-computational-diag
nostics/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-A
M.jpg\;200\;200\,medium\;http://web.stanford.edu/group/radweb/cgi-bin/radc
alendar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-AM
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ar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-AM.jpg\
;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Spyridon (Spyros) Bakas\, PhD \nAssistant P
rofessor in the Department of Pathology\, \nLaboratory Medicine\, and
of Radiology \nCenter for Biomedical Image Computing and Analytics (
CBICA) \nPerelman School of Medicine \nUniversity of Pennsylvani
a
\n
Title: Imaging Analytics for Neuro-Oncology: \nTowar
ds Computational Diagnostics
\n
Abstract: Central ne
rvous system (CNS) tumors come with vastly heterogeneous histologic\, mole
cular\, and radiographic landscapes\, rendering their precise characteriza
tion challenging. The rapidly growing fields of biophysical modeling and r
adiomics have shown promise in better characterizing the molecular\, spati
al\, and temporal heterogeneity of tumors. Integrative analysis of CNS tum
ors\, including clinically acquired multi-parametric magnetic resonance im
aging (mpMRI)\, assists in identifying macroscopic quantifiable tumor patt
erns of invasion and proliferation\, potentially leading to improved (a) d
etection/segmentation of tumor subregions and (b) computer-aided diagnosti
c/prognostic/predictive modeling. This talk will touch upon example studie
s on this space\, as well as an overview of the largest to-date real-world
federated learning study to detect brain tumor boundaries.
\n
HTML>
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3061@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:Harini Veeraraghavan\, PhD\nAssociate Attending Computer Scient
ist\nDepartment of Medical Physics\nMemorial Sloan-Kettering Cancer Center
\nUsing AI for Longitudinal Tumor Response Monitoring and AI Guided Cancer
Treatments: From Lab to Clinic\nAbstract:\nCancer patients are imaged wit
h multiple imaging modalities as part of routine cancer care. However\, th
e rich information available from the images are not fully exploited to be
tter manage patient care through earlier intervention as well as more prec
ise targeted treatments. In this talk\, I will present some of the new AI
methodologies we have been developing to track tumor response as well as f
rom routinely acquired imaging applied to image-guided radiation treatment
s using CT/cone-beam CT as well as MRI-guided precision treatments. I will
also present some demonstration studies of how AI based automated segment
ation and tumor as well as healthy tissue change assessment can be used to
early detect treatment toxicities to enable clinicians to better manage c
ancer care. Finally\, I will show how these developed methods have been pu
t to routine clinical care for automating radiotherapy treatment planning
at MSK.
DTSTART;TZID=America/Los_Angeles:20220316T120000
DTEND;TZID=America/Los_Angeles:20220316T130000
LOCATION:ZOOM: https://stanford.zoom.us/j/99319571697?pwd=c2lhRkN4cXEzTzFzM
UhKaTVJMHZLQT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Using AI for Longitudinal Tumor Response Moni
toring and AI Guided Cancer Treatments: From Lab to Clinic
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-using-ai-for-longitudinal-tumor-response-monitoring-and-ai-guid
ed-cancer-treatments-from-lab-to-clinic/
X-COST-TYPE:free
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X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Harini Veeraraghavan\, PhD \nAssociate Attending Computer Scient
ist \nDepartment of Medical Physics \nMemorial Sloan-Kettering C
ancer Center
\n
Using AI for Longitudinal Tumor Response Moni
toring and AI Guided Cancer Treatments: From Lab to Clinic
\n<
p>Abstract: \nCancer patients are imaged with multip
le imaging modalities as part of routine cancer care. However\, the rich i
nformation available from the images are not fully exploited to better man
age patient care through earlier intervention as well as more precise targ
eted treatments. In this talk\, I will present some of the new AI methodol
ogies we have been developing to track tumor response as well as from rout
inely acquired imaging applied to image-guided radiation treatments using
CT/cone-beam CT as well as MRI-guided precision treatments. I will also pr
esent some demonstration studies of how AI based automated segmentation an
d tumor as well as healthy tissue change assessment can be used to early d
etect treatment toxicities to enable clinicians to better manage cancer ca
re. Finally\, I will show how these developed methods have been put to rou
tine clinical care for automating radiotherapy treatment planning at MSK.<
/p>\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3071@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; rtotah@stanford.edu\; https://ibiis.stanford.edu/even
ts/seminars/2022seminars.html
DESCRIPTION:Spyridon (Spyros) Bakas\, PhD\nAssistant Professor in the Depar
tment of Pathology\,\nLaboratory Medicine\, and of Radiology\nCenter for B
iomedical Image Computing and Analytics (CBICA)\nPerelman School of Medici
ne\nUniversity of Pennsylvania\nTitle: Imaging Analytics for Neuro-Oncolog
y: Towards Computational Diagnostics\nCentral nervous system (CNS) tumors
come with vastly heterogeneous histologic\, molecular\, and radiographic l
andscapes\, rendering their precise characterization challenging. The rapi
dly growing fields of biophysical modeling and radiomics have shown promis
e in better characterizing the molecular\, spatial\, and temporal heteroge
neity of tumors. Integrative analysis of CNS tumors\, including clinically
acquired multi-parametric magnetic resonance imaging (mpMRI)\, assists in
identifying macroscopic quantifiable tumor patterns of invasion and proli
feration\, potentially leading to improved (a) detection/segmentation of t
umor subregions and (b) computer-aided diagnostic/prognostic/predictive mo
deling. This talk will touch upon example studies on this space\, as well
as an overview of the largest to-date real-world federated learning study
to detect brain tumor boundaries.
DTSTART;TZID=America/Los_Angeles:20220414T110000
DTEND;TZID=America/Los_Angeles:20220414T120000
LOCATION:Zoom: https://stanford.zoom.us/j/98789338790?pwd=OXRORjhYUUdaRGJpU
HJZdzZ5NGw5dz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Imaging Analytics for Neuro-Oncology: Towards
Computational Diagnostics
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-imaging-analytics-for-neuro-oncology-towards-computational-diag
nostics-2/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-A
M.jpg\;200\;200\,medium\;http://web.stanford.edu/group/radweb/cgi-bin/radc
alendar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-AM
.jpg\;200\;200\,large\;http://web.stanford.edu/group/radweb/cgi-bin/radcal
endar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-AM.j
pg\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalend
ar/wp-content/uploads/2022/02/bakas-aibil-headshot-160-215-8.37.55-AM.jpg\
;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Spyridon (Spyros) Bakas\, PhD \nAssistant P
rofessor in the Department of Pathology\, \nLaboratory Medicine\, and
of Radiology \nCenter for Biomedical Image Computing and Analytics (
CBICA) \nPerelman School of Medicine \nUniversity of Pennsylvani
a
\n
Title: Imaging Analytics for Neuro-Oncology: Towards Comp
utational Diagnostics
\n
Central nervous system (CNS) tumors come wit
h vastly heterogeneous histologic\, molecular\, and radiographic landscape
s\, rendering their precise characterization challenging. The rapidly grow
ing fields of biophysical modeling and radiomics have shown promise in bet
ter characterizing the molecular\, spatial\, and temporal heterogeneity of
tumors. Integrative analysis of CNS tumors\, including clinically acquire
d multi-parametric magnetic resonance imaging (mpMRI)\, assists in identif
ying macroscopic quantifiable tumor patterns of invasion and proliferation
\, potentially leading to improved (a) detection/segmentation of tumor sub
regions and (b) computer-aided diagnostic/prognostic/predictive modeling.
This talk will touch upon example studies on this space\, as well as an ov
erview of the largest to-date real-world federated learning study to detec
t brain tumor boundaries.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3069@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:
DESCRIPTION:Daniel Marcus\, PhD\nProfessor of Radiology\nDirector of the Ne
uroinformatics Research Group\nDirector of the Neuroimaging Informatics an
d Analysis Center\nWashington University\nAbstract:\nDeveloping and deploy
ing computational tools for neuro-oncology applications includes a sequenc
e of complex steps to identify appropriate images\, assess image quality\,
annotate\, process and other prepare and manipulate data for analysis. We
have implemented services and tools on the open source XNAT informatics p
latform to automate much of this workflow to improve both its efficiency a
nd effectiveness. Dr. Marcus will discuss this automated workflow and its
implementation in a number of data sets and applications at Washington Uni
versity.
DTSTART;TZID=America/Los_Angeles:20220420T120000
DTEND;TZID=America/Los_Angeles:20220420T130000
LOCATION:Zoom: https://stanford.zoom.us/j/94439662481?pwd=N1BUc2FqWUt4QVlQM
nNSS21rcEV4UT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Automated Workflows for Neuro-Oncology Image
Analysis
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-automated-workflows-for-neuro-oncology-image-analysis/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/04/Daniel-Marcus.jpg\;200\;200\,medium\;h
ttp://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads
/2022/04/Daniel-Marcus.jpg\;200\;200\,large\;http://web.stanford.edu/group
/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/04/Daniel-Marcus.jpg\;
200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/w
p-content/uploads/2022/04/Daniel-Marcus.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Daniel Marcus\, PhD \nProfessor of Radiology \nDirector of the Neuroinformatics Research Group \nDirector of th
e Neuroimaging Informatics and Analysis Center \nWashington Universit
y
\n
Abstract: \nDeveloping and deploying computational tools fo
r neuro-oncology applications includes a sequence of complex steps to iden
tify appropriate images\, assess image quality\, annotate\, process and ot
her prepare and manipulate data for analysis. We have implemented services
and tools on the open source XNAT informatics platform to automate much o
f this workflow to improve both its efficiency and effectiveness. Dr. Marc
us will discuss this automated workflow and its implementation in a number
of data sets and applications at Washington University.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3077@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; rtotah@stanford.edu\; https://ibiis.stanford.edu/even
ts/seminars/2022seminars.html
DESCRIPTION:Lena Maier-Hein\, PhD\nHead of Department\, Computer Assisted M
edical Interventions\nManaging Director\, Data Science and Digital Oncolog
y\nManaging Director\, National Center for Tumor Diseases\nGerman Cancer R
esearch Center\nTitle: Missing the (Bench)mark?\n\n\n\n\n\n\n\n\n\nAbstrac
t\n\n\n\n\n\n\n\nMachine learning has begun to revolutionize almost all ar
eas of health research. Success stories cover a wide variety of applicatio
n fields ranging from radiology and gastroenterology all the way to mental
health. Strikingly\, however\, solutions that perform favorably in resear
ch generally do not translate well to clinical practice\, and little atten
tion is being given to learning from failures. Focusing on biomedical imag
e analysis as a key area of health-related machine learning\, this talk wi
ll present pitfalls\, caveats and recommendations related to machine learn
ing-based biomedical image analysis. As a particular highlight\, it will c
over yet unpublished work on two key research questions related to biomedi
cal image analysis competitions: 1) How can we best select performance met
rics according to the characteristics of the driving biomedical question?
And 2) Why is the winner the best? The results have been compiled based on
the input of hundreds of image analysis researchers worldwide.
DTSTART;TZID=America/Los_Angeles:20220518T093000
DTEND;TZID=America/Los_Angeles:20220518T103000
LOCATION:Zoom: https://stanford.zoom.us/j/95872488712?pwd=dDhmT1JPdWtTSlBOQ
1BENmtGOUxjUT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Missing the (Bench)mark?
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-missing-the-benchmark/
X-COST-TYPE:free
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22/05/Meier-Hein.jpg\;200\;200\,large\;http://web.stanford.edu/group/radwe
b/cgi-bin/radcalendar/wp-content/uploads/2022/05/Meier-Hein.jpg\;200\;200\
,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content
/uploads/2022/05/Meier-Hein.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
\n
\n
\n
Lena Maier-Hein\,
PhD \nHead of Department\, Computer Assisted Medical Intervention
s \nManaging Director\, Data Science and Digital Oncology \nMana
ging Director\, National Center for Tumor Diseases \nGerman Cancer Re
search Center
\n
Title: Missing the (Bench)mark?
\n
\n
\n
\n
\n
\n
\n\n
\n
\n
Abstract
\n
\n
\n
\n
\n
\n
\n
\n
Machine learning has begun to revol
utionize almost all areas of health research. Success stories cover a wide
variety of application fields ranging from radiology and gastroenterology
all the way to mental health. Strikingly\, however\, solutions that perfo
rm favorably in research generally do not translate well to clinical pract
ice\, and little attention is being given to learning from failures. Focus
ing on biomedical image analysis as a key area of health-related machine l
earning\, this talk will present pitfalls\, caveats and recommendations re
lated to machine learning-based biomedical image analysis. As a particular
highlight\, it will cover yet unpublished work on two key research questi
ons related to biomedical image analysis competitions: 1) How can we best
select performance metrics according to the characteristics of the driving
biomedical question? And 2) Why is the winner the best? The results have
been compiled based on the input of hundreds of image analysis researchers
worldwide.
\n
\n
\n
\n
\n
\n
\n
\n<
/div>\n
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3083@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:Lauren Oakden-Rayner\, PhD\nDirector of Research in Medical Ima
ging\nRoyal Adelaide Hospital\nSenior Research Fellow\nAustralian Institut
e for Machine Learning\nTitle: Medical AI Safety – A Clinical Perspective
\nAbstract:\nMedical artificial intelligence is rapidly moving into clinic
s\, particularly in imaging-based specialties such as radiology. This tran
sition is producing many new challenges\, as the regulatory environment ha
s struggled to keep up and AI training for healthcare workers is virtually
non-existent. Dr. Oakden-Rayner will provide a clinical safety perspectiv
e on medical AI\, discuss a range of identified risks and potential harms\
, and discuss possible solutions to mitigate these risks as this exciting
field continues to develop.\nBio:\nDr. Lauren Oakden-Rayner (FRANZCR\, PhD
) is the Director of Research in Medical Imaging at the Royal Adelaide Hos
pital and is a senior research fellow at the Australian Institute for Mach
ine Learning. Her research explores the safe translation of artificial int
elligence technologies into clinical practice\, both from a technical and
clinical perspective.
DTSTART;TZID=America/Los_Angeles:20220616T160000
DTEND;TZID=America/Los_Angeles:20220616T170000
LOCATION:Zoom: https://stanford.zoom.us/j/93524639045?pwd=NUV1MFE2clBCYVp3K
0FJNlJFTGswdz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Medical AI Safety – A Clinical Perspective
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-medical-ai-safety-a-clinical-perspective/
X-COST-TYPE:free
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calendar/wp-content/uploads/2022/06/lauren-oakden-rayner.jpg\;200\;200\,me
dium\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/
uploads/2022/06/lauren-oakden-rayner.jpg\;200\;200\,large\;http://web.stan
ford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/06/laure
n-oakden-rayner.jpg\;200\;200\,full\;http://web.stanford.edu/group/radweb/
cgi-bin/radcalendar/wp-content/uploads/2022/06/lauren-oakden-rayner.jpg\;2
00\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
\nLauren Oakden-Rayner\, PhD
\nDirector of Research in Medical Imaging \nRoyal Adelaide Hospital\nSenior Research Fellow \nAustralian Institute for Machine Learn
ing
\n
Title: Medical AI Safety – A Clinical Perspec
tive
\n
Abstract: \nMedical artificial intelligence is ra
pidly moving into clinics\, particularly in imaging-based specialties such
as radiology. This transition is producing many new challenges\, as the r
egulatory environment has struggled to keep up and AI training for healthc
are workers is virtually non-existent. Dr. Oakden-Rayner will provide a cl
inical safety perspective on medical AI\, discuss a range of identified ri
sks and potential harms\, and discuss possible solutions to mitigate these
risks as this exciting field continues to develop.
\n
Bio:\nDr. Lauren Oakden-Rayner (FRANZCR\, PhD) is the Director of
Research in Medical Imaging at the Royal Adelaide Hospital and is a senio
r research fellow at the Australian Institute for Machine Learning. Her re
search explores the safe translation of artificial intelligence technologi
es into clinical practice\, both from a technical and clinical perspective
. \n
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3089@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:David Magnus\, PhD\nThomas A Raffin Professor of Medicine and B
iomedical Ethics and Professor of Pediatrics\, Medicine\, and by courtesy
of Bioengineering\nDirector\, Stanford Center for Biomedical Ethics\nAssoc
iate Dean for Research\nStanford University\nTitle: Ethical Challenges in
the Application of AI to Healthcare\nAbstract:\nThis presentation will foc
us on three issues. First\, applying AI to healthcare requires access to l
arge data sets. Data acquisition and data sharing raises a number of chall
enging ethical issues\, including challenges to traditional understandings
of informed consent\, and importance of diversity and inclusion in data s
ources. Second\, I will briefly discuss the widely discussed issues around
justice and equity raised by AI in healthcare. Finally\, I will discuss c
hallenges with ethical oversight and governance\, particularly in relation
to research development of AI. IRB’s are prohibited from considering down
stream social consequences and harms to individuals other than research pa
rticipants when evaluating the harms and risks of research. This gap needs
to be filled\, particularly as dual uses of AI models are now recognized
as a problem.\nBio: \nDavid Magnus\, PhD is Thomas A. Raffin Professor of
Medicine and Biomedical Ethics and Professor of Pediatrics and Medicine an
d by Courtesy of Bioengineering at Stanford University\, where he is Direc
tor of the Stanford Center for Biomedical Ethics and an Associate Dean of
Research. Magnus is member of the Ethics Committee for the Stanford Hospit
al. He is currently the Vice-Chair of the IRB for the NIH Precision Medici
ne Initiative (“All of Us”). He is the former President of the Association
of Bioethics Program Directors\, and is the Editor in Chief of the Americ
an Journal of Bioethics. He has published articles on a wide range of topi
cs in bioethics\, including research ethics\, genetics\, stem cell researc
h\, organ transplantation\, end of life\, and patient communication. He wa
s a member of the Secretary of Agriculture’s Advisory Committee on Biotech
nology in the 21st Century and currently serves on the California Human St
em Cell Research Advisory Committee. He is the principal editor of a colle
ction of essays entitled “Who Owns Life?” (2002) and his publications have
appeared in New England Journal of Medicine\, Science\, Nature Biotechnol
ogy\, and the British Medical Journal. He has appeared on many radio and t
elevision shows including 60 Minutes\, Good Morning America\, The Today Sh
ow\, CBS This Morning\, FOX news Sunday\, and ABC World News and NPR. In a
ddition to his scholarly work\, he has published Opinion pieces in the Phi
ladelphia Inquirer\, the Chicago Tribune\, the San Jose Mercury News\, and
the New Jersey Star Ledger.
DTSTART;TZID=America/Los_Angeles:20220921T133000
DTEND;TZID=America/Los_Angeles:20220921T143000
LOCATION:ZOOM: https://stanford.zoom.us/j/99191454207?pwd=N0ZYWnh1Mks0UEluO
VRUZjdWNHZPUT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Ethical Challenges in the Application of AI t
o Healthcare
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-ethical-challenges-in-the-application-of-ai-to-healthcare/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/09/david_magnus_ep_44_good.jpg\;200\;200\
,medium\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-conte
nt/uploads/2022/09/david_magnus_ep_44_good.jpg\;200\;200\,large\;http://we
b.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/09
/david_magnus_ep_44_good.jpg\;200\;200\,full\;http://web.stanford.edu/grou
p/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/09/david_magnus_ep_44
_good.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
David Magnus\, PhD \nThomas A Raffin Professor of Medicine and Biomedical Ethics and Profes
sor of Pediatrics\, Medicine\, and by courtesy of Bioengineering \nDi
rector\, Stanford Center for Biomedical Ethics \nAssociate Dean for R
esearch \nStanford University
\n
Title: Ethical Challenge
s in the Application of AI to Healthcare
\n
Abstract: \nT
his presentation will focus on three issues. First\, applying AI to health
care requires access to large data sets. Data acquisition and data sharing
raises a number of challenging ethical issues\, including challenges to t
raditional understandings of informed consent\, and importance of diversit
y and inclusion in data sources. Second\, I will briefly discuss the widel
y discussed issues around justice and equity raised by AI in healthcare. F
inally\, I will discuss challenges with ethical oversight and governance\,
particularly in relation to research development of AI. IRB’s are prohibi
ted from considering downstream social consequences and harms to individua
ls other than research participants when evaluating the harms and risks of
research. This gap needs to be filled\, particularly as dual uses of AI m
odels are now recognized as a problem.
\n
Bio: \nDavid Magnus\, PhD is Thomas A. Raffin Profe
ssor of Medicine and Biomedical Ethics and Professor of Pediatrics and Med
icine and by Courtesy of Bioengineering at Stanford University\, where he
is Director of the Stanford Center for Biomedical Ethics and an Associate
Dean of Research. Magnus is member of the Ethics Committee for the Stanfor
d Hospital. He is currently the Vice-Chair of the IRB for the NIH Precisio
n Medicine Initiative (“All of Us”). He is the former President of the Ass
ociation of Bioethics Program Directors\, and is the Editor in Chief of th
e American Journal of Bioethics. He has published articles on a wide range
of topics in bioethics\, including research ethics\, genetics\, stem cell
research\, organ transplantation\, end of life\, and patient communicatio
n. He was a member of the Secretary of Agriculture’s Advisory Committee on
Biotechnology in the 21st Century and currently serves on the California
Human Stem Cell Research Advisory Committee. He is the principal editor of
a collection of essays entitled “Who Owns Life?” (2002) and his publicati
ons have appeared in New England Journal of Medicine\, Science\, Nature Bi
otechnology\, and the British Medical Journal. He has appeared on many rad
io and television shows including 60 Minutes\, Good Morning America\, The
Today Show\, CBS This Morning\, FOX news Sunday\, and ABC World News and N
PR. In addition to his scholarly work\, he has published Opinion pieces in
the Philadelphia Inquirer\, the Chicago Tribune\, the San Jose Mercury Ne
ws\, and the New Jersey Star Ledger.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3093@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:Polina Golland\, PhD\nProfessor of Electrical Engineering and C
omputer Science\nPI in the Computer Science and Artificial Intelligence La
boratory\nMassachusetts Institute of Technology\nTitle: Learning to Read X
-Ray: Applications to Heart Failure Monitoring\nAbstract: We propose and d
emonstrate a novel approach to training image classification models based
on large collections of images with limited labels. We take advantage of a
vailability of radiology reports to construct joint multimodal embedding t
hat serves as a basis for classification. We demonstrate the advantages of
this approach in application to assessment of pulmonary edema severity in
congestive heart failure that motivated the development of the method.
DTSTART;TZID=America/Los_Angeles:20221019T120000
DTEND;TZID=America/Los_Angeles:20221019T130000
LOCATION:ZOOM: https://stanford.zoom.us/j/93555578704?pwd=eTdhRHM4K0w5WGVmS
ElSWGkzN3VqQT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Learning to Read X-Ray: Applications to Heart
Failure Monitoring
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-learning-to-read-x-ray-applications-to-heart-failure-monitoring
/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/09/Polina-Golland.jpg\;200\;200\,medium\;
http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/upload
s/2022/09/Polina-Golland.jpg\;200\;200\,large\;http://web.stanford.edu/gro
up/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/09/Polina-Golland.jp
g\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalenda
r/wp-content/uploads/2022/09/Polina-Golland.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Polina Golland\, PhD \nProfessor of Electric
al Engineering and Computer Science \nPI in the Computer Science and
Artificial Intelligence Laboratory \nMassachusetts Institute of Techn
ology
\n
Title: Learning to Read X-Ray: Applications to Heart
Failure Monitoring
\n
Abstract: We propose and demonstrate a n
ovel approach to training image classification models based on large colle
ctions of images with limited labels. We take advantage of availability of
radiology reports to construct joint multimodal embedding that serves as
a basis for classification. We demonstrate the advantages of this approach
in application to assessment of pulmonary edema severity in congestive he
art failure that motivated the development of the method.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3100@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:Baris Turkbey\, MD\, FSAR\nSenior Clinician\nSection Chief of M
RI\nSection Chief of Artificial Intelligence\nMolecular Imaging Branch\nNa
tional Cancer Institute\, NIH\nTitle: Advanced Prostate Cancer Imaging\nTa
lk Objectives: \n\nTo discuss current status and limitations of localized
prostate cancer diagnosis.\nTo discuss use of artificial intelligence in d
iagnosis of localized prostate cancer.\nTo discuss use of molecular imagin
g in clinical prostate cancer management.\n\nBio:\nDr. Turkbey obtained hi
s medical degree from Hacettepe University in Ankara\, Turkey in 2003. He
completed his residency in Diagnostic and Interventional Radiology at Hace
ttepe University. He joined Molecular Imaging Branch (MIB)\, National Canc
er Institute\, NIH in 2007. His main research areas are imaging of prostat
e cancer (multiparametric MRI\, PET CT)\, image guided biopsy and treatmen
t techniques (focal therapy\, surgery and radiation therapy) for prostate
cancer and artificial intelligence. Dr. Turkbey is a member of Prostate Im
aging Reporting & Data System (PI-RADS) Steering Committee. He is the Dire
ctor Magnetic Resonance Imaging section in MIB and the Artificial Intellig
ence Resource in MIB.
DTSTART;TZID=America/Los_Angeles:20221116T120000
DTEND;TZID=America/Los_Angeles:20221116T130000
LOCATION:Zoom: https://stanford.zoom.us/j/99807942044?pwd=TmJkclNkbVBZOG04K
zJaSFRWVXlxZz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Advanced Prostate Cancer Imaging
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-advanced-prostate-cancer-imaging/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2022/11/Turkbey.jpg\;200\;200\,medium\;http://
web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2022/
11/Turkbey.jpg\;200\;200\,large\;http://web.stanford.edu/group/radweb/cgi-
bin/radcalendar/wp-content/uploads/2022/11/Turkbey.jpg\;200\;200\,full\;ht
tp://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/
2022/11/Turkbey.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Baris Turkbey\, MD\, FSAR \nSenior Clinician \nSection Chief
of MRI \nSection Chief of Artificial Intelligence \nMolecular I
maging Branch \nNational Cancer Institute\, NIH
\n
Title
: Advanced Prostate Cancer Imaging
\n
Talk Objectives:
b>
\n
\n
To discuss current status and limitations of localized p
rostate cancer diagnosis.
\n
To discuss use of artificial intellige
nce in diagnosis of localized prostate cancer.
\n
To discuss use of
molecular imaging in clinical prostate cancer management.
\n
\n
Bio: \nDr. Turkbey obtained his medical degree from
Hacettepe University in Ankara\, Turkey in 2003. He completed his residen
cy in Diagnostic and Interventional Radiology at Hacettepe University. He
joined Molecular Imaging Branch (MIB)\, National Cancer Institute\, NIH in
2007. His main research areas are imaging of prostate cancer (multiparame
tric MRI\, PET CT)\, image guided biopsy and treatment techniques (focal t
herapy\, surgery and radiation therapy) for prostate cancer and artificial
intelligence. Dr. Turkbey is a member of Prostate Imaging Reporting & Dat
a System (PI-RADS) Steering Committee. He is the Director Magnetic Resonan
ce Imaging section in MIB and the Artificial Intelligence Resource in MIB.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3106@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2022seminars.html
DESCRIPTION:In Person at the Clark Center S360 – Lunch will be provided!\nZ
oom: https://stanford.zoom.us/j/99496515255?pwd=MHlXbXM2WXJULzZwemk1WjJHNF
ZOdz09\n\nAnthony Gatti\, PhD\nPostdoctoral Research Fellow\nDepartment of
Radiology\nWu Tsai Human Performance Alliance\nStanford University\n\n\n
\nTitle: Towards Understanding Knee Health Using Automated MRI-Based Stati
stical Shape Models\n\n\n\n\n\n\n\n\n\nAbstract: Knee injuries and pain ar
e prevalent across all ages\, with varying causes from “anterior knee pain
” in runners to osteoarthritis-related pain. Osteoarthritis pain is a part
icular problem because structural outcomes assessed on medical images ofte
n disagree with symptoms. Most studies trying to understand knee health an
d pain use simple biomarkers such as mean cartilage thickness. My talk wil
l present an automated pipeline for quantifying the whole knee using stati
stical shape modeling. I will present a conventional statistical shape mod
el as well as a novel approach that uses generative neural implicit repres
entations. Both modeling approaches allow unsupervised identification of s
alient anatomic features. I will demonstrate how these features can be use
d to predict existing radiographic outcomes\, patient demographics\, and k
nee pain.\n\n\n\n\nLiangqiong Qu\, PhD\nPostdoctoral Research Fellow\nDepa
rtment of Biomedical Data Sciences\nStanford University\nTitle: Distribute
d Deep Learning in Medical Imaging\n\n\n\n\n\n\n\n\n\nAbstract: Distribute
d deep learning is an emerging research paradigm for enabling collaborativ
ely training deep learning models without sharing patient data.\nIn this t
alk\, we will first investigate the use distributed deep learning to build
medical imaging classification models in a real-world collaborative setti
ng.\nWe then present several strategies to tackle the data heterogeneity c
hallenge and the lack of quality labeled data challenge in distributed dee
p learning.
DTSTART;TZID=America/Los_Angeles:20221214T130000
DTEND;TZID=America/Los_Angeles:20221214T140000
LOCATION:Clark Center S360 @ 318 Campus Drive\, Stanford\, CA
SEQUENCE:0
SUMMARY:IBIIS & AIMI Hybrid Seminar: Anthony Gatti\, PhD & Liangqiong Qu\,
PhD
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-hybrid-seminar-anthony-gatti-phd-liangqiong-qu-phd/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
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ttp://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads
/2022/12/Anthony-Gatti.jpg\;200\;200\,large\;http://web.stanford.edu/group
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200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/w
p-content/uploads/2022/12/Anthony-Gatti.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
In Pe
rson at the Clark Center S360 – Lunch will be provided! \nZoom: https://stanford.zoom.us/j/99496515255?pwd=MHlXbXM2WX
JULzZwemk1WjJHNFZOdz09
\n\n
Anthony Gatt
i\, PhD \nPostdoctoral Research Fellow \nDepartment of Radio
logy \nWu Tsai Human Performance Alliance \nStanford University<
/p>\n
\n
\n
\n
Title: Towar
ds Understanding Knee Health Using Automated MRI-Based Statistical Shape M
odels
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
Abstract: Knee
injuries and pain are prevalent across all ages\, with varying causes fro
m “anterior knee pain” in runners to osteoarthritis-related pain. Osteoart
hritis pain is a particular problem because structural outcomes assessed o
n medical images often disagree with symptoms. Most studies trying to unde
rstand knee health and pain use simple biomarkers such as mean cartilage t
hickness. My talk will present an automated pipeline for quantifying the w
hole knee using statistical shape modeling. I will present a conventional
statistical shape model as well as a novel approach that uses generative n
eural implicit representations. Both modeling approaches allow unsupervise
d identification of salient anatomic features. I will demonstrate how thes
e features can be used to predict existing radiographic outcomes\, patient
demographics\, and knee pain.
\n\n<
div class='text-image section'>\n
\n
\n
Liangqiong Qu\, PhD \nPostdoctoral Research Fellow \nDepartment of Biomedical Data
Sciences \nStanford University
\n
Title: Distributed Dee
p Learning in Medical Imaging
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
Abstract: Distributed deep learning is an emerging res
earch paradigm for enabling collaboratively training deep learning models
without sharing patient data. \nIn this talk\, we will first investig
ate the use distributed deep learning to build medical imaging classificat
ion models in a real-world collaborative setting. \nWe then present s
everal strategies to tackle the data heterogeneity challenge and the lack
of quality labeled data challenge in distributed deep learning.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3114@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 4088131312\; ramzitotah@gmail.com\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Archana Venkataraman\, PhD\nAssociate Professor of Electrical a
nd Computer Engineering\nBoston University\nTitle: Biologically Inspired D
eep Learning as a New Window into Brain Dysfunction\nAbstract: Deep learni
ng has disrupted nearly every major field of study from computer vision to
genomics. The unparalleled success of these models has\, in many cases\,
been fueled by an explosion of data. Millions of labeled images\, thousand
s of annotated ICU admissions\, and hundreds of hours of transcribed speec
h are common standards in the literature. Clinical neuroscience is a notab
le holdout to this trend. It is a field of unavoidably small datasets\, ma
ssive patient variability\, and complex (largely unknown) phenomena. My la
b tackles these challenges across a spectrum of projects\, from answering
foundational neuroscientific questions to translational applications of ne
uroimaging data to exploratory directions for probing neural circuitry. On
e of our key strategies is to integrate a priori information about the bra
in and biology into the model design.\nThis talk will highlight two ongoin
g projects that epitomize this strategy. First\, I will showcase an end-to
-end deep learning framework that fuses neuroimaging\, genetic\, and pheno
typic data\, while maintaining interpretability of the extracted biomarker
s. We use a learnable dropout layer to extract a sparse subset of predicti
ve imaging features and a biologically informed deep network architecture
for whole-genome analysis. Specifically\, the network uses hierarchical gr
aph convolution that mimic the organization of a well-established gene ont
ology to track the convergence of genetic risk across biological pathways.
Second\, I will present a deep-generative hybrid model for epileptic seiz
ure detection from scalp EEG. The latent variables in this model capture t
he spatiotemporal spread of a seizure\; they are complemented by a nonpara
metric likelihood based on convolutional neural networks. I will also high
light our current end-to-end extensions of this work focused on seizure on
set localization. Finally\, I will conclude with exciting future direction
s for our work across the foundational\, translational\, and exploratory a
xes.
DTSTART;TZID=America/Los_Angeles:20230118T120000
DTEND;TZID=America/Los_Angeles:20230118T130000
LOCATION:Zoom: https://stanford.zoom.us/j/96155849129?pwd=MTVtenF6RWdHMEwwd
EZoV3NhM0svUT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Zoom Seminar: Biologically Inspired Deep Learning as a
New Window into Brain Dysfunction
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-biologically-inspired-deep-learning-as-a-new-window-into-brain-
dysfunction/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/01/Picture1-298x300.jpg\;298\;300\,medium
\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uplo
ads/2023/01/Picture1-298x300.jpg\;298\;300\,large\;http://web.stanford.edu
/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/01/Picture1-298x
300.jpg\;298\;300\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radc
alendar/wp-content/uploads/2023/01/Picture1-298x300.jpg\;298\;300
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Archana
Venkataraman\, PhD \nAssociate Professor of Electrical and Compu
ter Engineering \nBoston University
\n
Title: Biologicall
y Inspired Deep Learning as a New Window into Brain Dysfunction
\n
Abstract: Deep learning has disrupted nearly every major fi
eld of study from computer vision to genomics. The unparalleled success of
these models has\, in many cases\, been fueled by an explosion of data. M
illions of labeled images\, thousands of annotated ICU admissions\, and hu
ndreds of hours of transcribed speech are common standards in the literatu
re. Clinical neuroscience is a notable holdout to this trend. It is a fiel
d of unavoidably small datasets\, massive patient variability\, and comple
x (largely unknown) phenomena. My lab tackles these challenges across a sp
ectrum of projects\, from answering foundational neuroscientific questions
to translational applications of neuroimaging data to exploratory directi
ons for probing neural circuitry. One of our key strategies is to integrat
e a priori information about the brain and biology into the model d
esign.
\n
This talk will highlight two ongoing projects that epitomiz
e this strategy. First\, I will showcase an end-to-end deep learning frame
work that fuses neuroimaging\, genetic\, and phenotypic data\, while maint
aining interpretability of the extracted biomarkers. We use a learnable dr
opout layer to extract a sparse subset of predictive imaging features and
a biologically informed deep network architecture for whole-genome analysi
s. Specifically\, the network uses hierarchical graph convolution that mim
ic the organization of a well-established gene ontology to track the conve
rgence of genetic risk across biological pathways. Second\, I will present
a deep-generative hybrid model for epileptic seizure detection from scalp
EEG. The latent variables in this model capture the spatiotemporal spread
of a seizure\; they are complemented by a nonparametric likelihood based
on convolutional neural networks. I will also highlight our current end-to
-end extensions of this work focused on seizure onset localization. Finall
y\, I will conclude with exciting future directions for our work across th
e foundational\, translational\, and exploratory axes.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3120@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Andrew Janowczyk\, PhD\nAssistant Professor\nDepartment of Biom
edical Engineering\nEmory University\nTitle: Computational Pathology: Towa
rds Precision Medicine\nAbstract:\nRoughly 40% of the population will be d
iagnosed with some form of cancer in their lifetime. In a large majority o
f these cases\, a definitive cancer diagnosis is only possible via histopa
thologic confirmation on a tissue slide. With the increasing popularity of
the digitization of pathology slides\, a wealth of new untapped data is n
ow regularly being created.\nComputational analysis of these routinely cap
tured H&E slides is facilitating the creation of diagnostic tools for task
s such as disease identification and grading. Further\, by identifying pat
terns of disease presentation across large cohorts of retrospectively anal
yzed patients\, new insights for predicting prognosis and therapy response
are possible [1\,2]. Such biomarkers\, derived from inexpensive histology
slides\, stand to improve the standard of care for all patient population
s\, especially where expensive genomic testing may not be readily availabl
e. Moreover\, since numerous other diseases and disorders\, such as oncomi
ng clinical heart failure [3]\, are similarly diagnosed via pathology slid
es\, those patients also stand to benefit from these same technological ad
vances in the digital pathology space.\nThis talk will discuss our researc
h aimed towards reaching the goal of precision medicine\, wherein patients
receive optimized treatment based on historical evidence. The talk discus
ses how the applications of deep learning in this domain are significantly
improving the efficiency and robustness of these models [4]. Numerous cha
llenges remain\, though\, especially in the context of quality control and
annotation gathering. This talk further introduces the audience to open-s
ource tools being developed and deployed to meet these pressing needs\, in
cluding quality control (histoqc.com [5])\, annotation (quickannotator.com
)\, labeling (patchsorter.com)\, validation (cohortfinder.com).
DTSTART;TZID=America/Los_Angeles:20230215T093000
DTEND;TZID=America/Los_Angeles:20230215T103000
LOCATION:Zoom: https://stanford.zoom.us/j/91585038349?pwd=L1ZuRkZibG1iSmdtR
0RtakhVdi9HZz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Computational Pathology – Towards Precision M
edicine
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-computational-pathology-towards-precision-medicine/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/02/janowczyk-andrew.jpg\;200\;200\,medium
\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uplo
ads/2023/02/janowczyk-andrew.jpg\;200\;200\,large\;http://web.stanford.edu
/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/02/janowczyk-and
rew.jpg\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radc
alendar/wp-content/uploads/2023/02/janowczyk-andrew.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Andrew Janowczyk\, PhD \nAssistant Pro
fessor \nDepartment of Biomedical Engineering \nEmory University
\n
Title: Computational Pathology: Towards Precision Medicine
\n
Abstract: \nRoughly 40% of the population will be dia
gnosed with some form of cancer in their lifetime. In a large majority of
these cases\, a definitive cancer diagnosis is only possible via histopath
ologic confirmation on a tissue slide. With the increasing popularity of t
he digitization of pathology slides\, a wealth of new untapped data is now
regularly being created.
\n
Computational analysis of these routinel
y captured H&E slides is facilitating the creation of diagnostic tools for
tasks such as disease identification and grading. Further\, by identifyin
g patterns of disease presentation across large cohorts of retrospectively
analyzed patients\, new insights for predicting prognosis and therapy res
ponse are possible [1\,2]. Such biomarkers\, derived from inexpensive hist
ology slides\, stand to improve the standard of care for all patient popul
ations\, especially where expensive genomic testing may not be readily ava
ilable. Moreover\, since numerous other diseases and disorders\, such as o
ncoming clinical heart failure [3]\, are similarly diagnosed via pathology
slides\, those patients also stand to benefit from these same technologic
al advances in the digital pathology space.
\n
This talk will discuss
our research aimed towards reaching the goal of precision medicine\, wher
ein patients receive optimized treatment based on historical evidence. The
talk discusses how the applications of deep learning in this domain are s
ignificantly improving the efficiency and robustness of these models [4].
Numerous challenges remain\, though\, especially in the context of quality
control and annotation gathering. This talk further introduces the audien
ce to open-source tools being developed and deployed to meet these pressin
g needs\, including quality control (histoqc.com [5])\, annotation (quicka
nnotator.com)\, labeling (patchsorter.com)\, validation (cohortfinder.com)
.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3128@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Melissa McCradden\, PhD\nJohn and Melinda Thompson Director of
Artificial Intelligence in Medicine\nIntegration Lead\, AI in Medicine Ini
tiative\nBioethicist\, The Hospital for Sick Children (SickKids)\nAssociat
e Scientist\, Genetics & Genome Biology\nAssistant Professor\, Dalla Lana
School of Public Health\nTitle: What Makes a ‘Good’ Decision? An Empirical
Bioethics Study of Using AI at the Bedside\nAbstract: This presentation w
ill identify the gap between AI accuracy and making good clinical decision
s. I will present a study where we develop an ethical framework for clinic
al decision-making that can help clinicians meet medicolegal and ethical s
tandards when using AI that does not rely on explainability\, nor perfect
accuracy of the model.
DTSTART;TZID=America/Los_Angeles:20230315T120000
DTEND;TZID=America/Los_Angeles:20230315T130000
LOCATION:https://stanford.zoom.us/j/96612401401?pwd=WFNJb2Q4dStoVDE5a25BYTB
kMjN4QT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: What Makes a ‘Good’ Decision? An Empirical Bi
oethics Study of Using AI at the Bedside
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-what-makes-a-good-decision-an-empirical-bioethics-study-of-usin
g-ai-at-the-bedside/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/03/Screen-Shot-2023-03-06-at-10.12.28-AM-
247x300.png\;247\;300\,medium\;http://web.stanford.edu/group/radweb/cgi-bi
n/radcalendar/wp-content/uploads/2023/03/Screen-Shot-2023-03-06-at-10.12.2
8-AM-247x300.png\;247\;300\,large\;http://web.stanford.edu/group/radweb/cg
i-bin/radcalendar/wp-content/uploads/2023/03/Screen-Shot-2023-03-06-at-10.
12.28-AM-247x300.png\;247\;300\,full\;http://web.stanford.edu/group/radweb
/cgi-bin/radcalendar/wp-content/uploads/2023/03/Screen-Shot-2023-03-06-at-
10.12.28-AM-247x300.png\;247\;300
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
\n
Melissa McCradden\, PhD \nJohn and Melinda Thompson Director of Artificial Intelligence in Medic
ine \nIntegration Lead\, AI in Medicine Initiative \nBioethicist
\, The Hospital for Sick Children (SickKids) \nAssociate Scientist\,
Genetics & Genome Biology \nAssistant Professor\, Dalla Lana School o
f Public Health
\n
Title: What Makes a ‘Good’ Decision? An Emp
irical Bioethics Study of Using AI at the Bedside
\n
Abstract:
This presentation will identify the gap between AI accuracy and making go
od clinical decisions. I will present a study where we develop an ethical
framework for clinical decision-making that can help clinicians meet medic
olegal and ethical standards when using AI that does not rely on explainab
ility\, nor perfect accuracy of the model.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3134@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Marzyeh Ghassemi\, PhD\nAssistant Professor\, Department of Ele
ctrical Engineering and Computer Science\nInstitute for Medical Engineerin
g & Science\nMassachusetts Institute of Technology (MIT)\nCanadian CIFAR A
I Chair at Vector Institute\nTitle: Designing Machine Learning Processes F
or Equitable Health Systems\nAbstract\nDr. Marzyeh Ghassemi focuses on cre
ating and applying machine learning to understand and improve health in wa
ys that are robust\, private and fair. Dr. Ghassemi will talk about her wo
rk trying to train models that do not learn biased rules or recommendation
s that harm minorities or minoritized populations. The Healthy ML group ta
ckles the many novel technical opportunities for machine learning in healt
h\, and works to make important progress with careful application to this
domain.
DTSTART;TZID=America/Los_Angeles:20230419T120000
DTEND;TZID=America/Los_Angeles:20230419T130000
LOCATION:ZOOM: https://stanford.zoom.us/j/97119304595?pwd=clBRVW45NXdWZE9ZU
k8ySzQ0OEtYQT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Hybrid Seminar: Designing Machine Learning Processes F
or Equitable Health Systems
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-hybrid-seminar-designing-machine-learning-processes-for-equitable-healt
h-systems/
X-COST-TYPE:free
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calendar/wp-content/uploads/2023/03/marzyeh-768x768.jpg\;200\;200\,medium\
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ds/2023/03/marzyeh-768x768.jpg\;200\;200\,large\;http://web.stanford.edu/g
roup/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/03/marzyeh-768x768
.jpg\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcale
ndar/wp-content/uploads/2023/03/marzyeh-768x768.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Marzyeh Ghassemi\, PhD \nAssistant Profes
sor\, Department of Electrical Engineering and Computer Science \nIns
titute for Medical Engineering & Science \nMassachusetts Institute of
Technology (MIT) \nCanadian CIFAR AI Chair at Vector Institute
\n
Title: Designing Machine Learning Processes For Equitable Health
Systems
\n
Abstract \nDr. Marzyeh Ghassemi fo
cuses on creating and applying machine learning to understand and improve
health in ways that are robust\, private and fair. Dr. Ghassemi will talk
about her work trying to train models that do not learn biased rules or re
commendations that harm minorities or minoritized populations. The Healthy
ML group tackles the many novel technical opportunities for machine learn
ing in health\, and works to make important progress with careful applicat
ion to this domain.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3138@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Hoifung Poon\nHoifung Poon\, PhD\nGeneral Manager at Health Fut
ures of Microsoft Research\nAffiliated Professor at the University of Wash
ington Medical School.\nTitle: Advancing Health at the Speed of AI\n\n\n\n
\n\n\n\n\n\nAbstract: The dream of precision health is to develop a data-d
riven\, continuous learning system where new health information is instant
ly incorporated to optimize care delivery and accelerate biomedical discov
ery. In reality\, however\, the health ecosystem is plagued by overwhelmin
g unstructured data and unscalable manual processing. Self-supervised AI s
uch as large language models (LLMs) can supercharge structuring of biomedi
cal data and accelerate transformation towards precision health. In this t
alk\, I’ll present our research progress on biomedical AI for precision he
alth\, spanning biomedical LLMs\, multi-modal learning\, and causal discov
ery. This enables us to extract knowledge from tens of millions of publica
tions\, structure real-world data for millions of cancer patients\, and ap
ply the extracted knowledge and real-world evidence to advancing precision
oncology in deep partnerships with real-world stakeholders.
DTSTART;TZID=America/Los_Angeles:20230426T143000
DTEND;TZID=America/Los_Angeles:20230426T153000
LOCATION:LKSC 120 and remote via Zoom @ https://stanford.zoom.us/j/92666973
395?pwd=SHpzVmVPMEFYRXQ5Skp5eG1vcXBrdz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Advancing Health at the Speed of AI
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-advancing-health-at-the-speed-of-ai/
X-COST-TYPE:free
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calendar/wp-content/uploads/2023/04/Hoifung-Poon-PhD.jpg\;200\;198\,medium
\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uplo
ads/2023/04/Hoifung-Poon-PhD.jpg\;200\;198\,large\;http://web.stanford.edu
/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/04/Hoifung-Poon-
PhD.jpg\;200\;198\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radc
alendar/wp-content/uploads/2023/04/Hoifung-Poon-PhD.jpg\;200\;198
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n
\n
\n
\n\n
Hoifung Poon\, PhD
\nGeneral Manager at Health Futures of Microsoft Research
\nAffiliated Professor at the University of Washington Medical School.
\n
Title: Advancing Health at the Speed of AI
\n
\n
\n
\n
\n
\n
\n<
div class='panel panel-default'>\n
\n
\n
Abstract: The dream of precision
health is to develop a data-driven\, continuous learning system where new
health information is instantly incorporated to optimize care delivery an
d accelerate biomedical discovery. In reality\, however\, the health ecosy
stem is plagued by overwhelming unstructured data and unscalable manual pr
ocessing. Self-supervised AI such as large language models (LLMs) can supe
rcharge structuring of biomedical data and accelerate transformation towar
ds precision health. In this talk\, I’ll present our research progress on
biomedical AI for precision health\, spanning biomedical LLMs\, multi-moda
l learning\, and causal discovery. This enables us to extract knowledge fr
om tens of millions of publications\, structure real-world data for millio
ns of cancer patients\, and apply the extracted knowledge and real-world e
vidence to advancing precision oncology in deep partnerships with real-wor
ld stakeholders.
\n
\n
\n
\n
\n
\n\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3144@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Despina Kontos\, PhD\nMatthew J. Wilson Professor of Research R
adiology II\nAssociate Vice-Chair for Research\, Department of Radiology\n
Perelman School of Medicine\nUniversity of Pennsylvania\nTitle: Radiomics
and Radiogenomics: The Role of Imaging\, Machine Learning\, and AI\, as a
Biomarker for Cancer Prognostication and Therapy Response Evaluation\nAbst
ract: Cancer is a heterogeneous disease\, with known inter-tumor and intra
-tumor heterogeneity in solid tumors. Established histopathologic prognost
ic biomarkers generally acquired from a tumor biopsy may be limited by sam
pling variation. Radiomics is an emerging field with the potential to leve
rage the whole tumor via non-invasive sampling afforded by medical imaging
to extract high throughput\, quantitative features for personalized tumor
characterization. Identifying imaging phenotypes via radiomics analysis a
nd understanding their relationship with prognostic markers and patient ou
tcomes can allow for a non-invasive assessment of tumor heterogeneity. Rec
ent studies have shown that intrinsic radiomic phenotypes of tumor heterog
eneity for cancer may have independent prognostic value when predicting di
sease aggressiveness and recurrence. The independent prognostic value of i
maging heterogeneity phenotypes suggests that radiogenomic phenotypes can
provide a non-invasive characterization of tumor heterogeneity to augment
genomic assays in precision prognosis and treatment.
DTSTART;TZID=America/Los_Angeles:20230517T120000
DTEND;TZID=America/Los_Angeles:20230517T130000
LOCATION:Clark Center S360 - Zoom Details on IBIIS website @ 318 Campus Dri
ve
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Radiomics and Radiogenomics: The Role of Imag
ing\, Machine Learning\, and AI\, as a Biomarker for Cancer Prognosticatio
n and Therapy Response Evaluation
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-radiomics-and-radiogenomics-the-role-of-imaging-machine-learnin
g-and-ai-as-a-biomarker-for-cancer-prognostication-and-therapy-response-ev
aluation/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/05/kont4311.jpg\;200\;200\,medium\;http:/
/web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2023
/05/kont4311.jpg\;200\;200\,large\;http://web.stanford.edu/group/radweb/cg
i-bin/radcalendar/wp-content/uploads/2023/05/kont4311.jpg\;200\;200\,full\
;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploa
ds/2023/05/kont4311.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Despina Kontos\, PhD \nMatthew J. Wilson Professor of Research
Radiology II \nAssociate Vice-Chair for Research\, Department of Rad
iology \nPerelman School of Medicine \nUniversity of Pennsylvani
a
\n
Title: Radiomics and Radiogenomics: The Role of Imaging\,
Machine Learning\, and AI\, as a Biomarker for Cancer Prognostication and
Therapy Response Evaluation
\n
Abstract: Cancer is a heteroge
neous disease\, with known inter-tumor and intra-tumor heterogeneity in so
lid tumors. Established histopathologic prognostic biomarkers generally ac
quired from a tumor biopsy may be limited by sampling variation. Radiomics
is an emerging field with the potential to leverage the whole tumor via n
on-invasive sampling afforded by medical imaging to extract high throughpu
t\, quantitative features for personalized tumor characterization. Identif
ying imaging phenotypes via radiomics analysis and understanding their rel
ationship with prognostic markers and patient outcomes can allow for a non
-invasive assessment of tumor heterogeneity. Recent studies have shown tha
t intrinsic radiomic phenotypes of tumor heterogeneity for cancer may have
independent prognostic value when predicting disease aggressiveness and r
ecurrence. The independent prognostic value of imaging heterogeneity pheno
types suggests that radiogenomic phenotypes can provide a non-invasive cha
racterization of tumor heterogeneity to augment genomic assays in precisio
n prognosis and treatment.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3150@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Daguang Xu\, PhD\nSenior Research Manager\nNVIDIA Healthcare\nT
itle: Industrial Applied Research in Healthcare and Federated Learning at
NVIDIA\nAbstract: As the market leader in deep learning and parallel compu
ting\, NVIDIA is fully committed to advancing applied research in medical
imaging. Our goal is to revolutionize the capabilities of medical doctors
and radiologists by equipping them with powerful tools and applications ba
sed on deep learning. We firmly believe that the integration of deep learn
ing and accelerated AI will have a profound impact on the life sciences\,
medicine\, and the healthcare industry as a whole. To drive this transform
ative process\, NVIDIA is actively democratizing deep learning through the
provision of a comprehensive AI computing platform specifically designed
for the healthcare community. These GPU-accelerated solutions not only pro
mote collaboration but also prioritize the security of each institution’s
information. By doing so\, we are fostering a collective effort in harness
ing the potential of deep learning to benefit healthcare.\nDuring this tal
k\, I will showcase remarkable research achievements accomplished by NVIDI
A’s deep learning in medical imaging team. This includes breakthroughs in
segmentation\, self-supervised learning\, federated learning\, and other r
elated areas. Additionally\, I will provide insights into the exciting ave
nues of research that our team is currently exploring.
DTSTART;TZID=America/Los_Angeles:20230621T120000
DTEND;TZID=America/Los_Angeles:20230621T130000
LOCATION:ZOOM: https://stanford.zoom.us/j/92801875181?pwd=TzAyOXd2UDJwYjJkc
2xQZ1doaXA3QT09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Industrial Applied Research in Healthcare and
Federated Learning at NVIDIA
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-industrial-applied-research-in-healthcare-and-federated-learnin
g-at-nvidia/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/06/DaguangXu-225x300.jpg\;225\;300\,mediu
m\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/upl
oads/2023/06/DaguangXu-225x300.jpg\;225\;300\,large\;http://web.stanford.e
du/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/06/DaguangXu-2
25x300.jpg\;225\;300\,full\;http://web.stanford.edu/group/radweb/cgi-bin/r
adcalendar/wp-content/uploads/2023/06/DaguangXu-225x300.jpg\;225\;300
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Daguang
Xu\, PhD \nSenior Research Manager \nNVIDIA Healthcare
\n
Title: Industrial Applied Research in Healthcare and Federated
Learning at NVIDIA
\n
Abstract: As the market leader in deep
learning and parallel computing\, NVIDIA is fully committed to advancing a
pplied research in medical imaging. Our goal is to revolutionize the capab
ilities of medical doctors and radiologists by equipping them with powerfu
l tools and applications based on deep learning. We firmly believe that th
e integration of deep learning and accelerated AI will have a profound imp
act on the life sciences\, medicine\, and the healthcare industry as a who
le. To drive this transformative process\, NVIDIA is actively democratizin
g deep learning through the provision of a comprehensive AI computing plat
form specifically designed for the healthcare community. These GPU-acceler
ated solutions not only promote collaboration but also prioritize the secu
rity of each institution’s information. By doing so\, we are fostering a c
ollective effort in harnessing the potential of deep learning to benefit h
ealthcare.
\n
During this talk\, I will showcase remarkable research
achievements accomplished by NVIDIA’s deep learning in medical imaging tea
m. This includes breakthroughs in segmentation\, self-supervised learning\
, federated learning\, and other related areas. Additionally\, I will prov
ide insights into the exciting avenues of research that our team is curren
tly exploring.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3156@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Negar Golestani\, PhD\nPostdoctoral Research Fellow\nDepartment
of Radiology\nStanford University\n\nTitle: AI in Radiology-Pathology Fus
ion Towards Precise Breast Cancer Detection\nAbstract: Breast cancer is a
global public health concern with various treatment options based on tumor
characteristics. Pathological examination of excised tissue after surgery
provides important information for treatment decisions. This pathology pr
ocessing involving the manual selection of representative sections for his
tological examination is time-consuming and subjective\, which can lead to
potential sampling errors. Accurately identifying residual tumors is a ch
allenging task\, which highlights the need for systematic or assisted meth
ods. Radiology-pathology registration is essential for developing deep-lea
rning algorithms to automate cancer detection on radiology images. However
\, aligning faxitron and histopathology images is difficult due to content
and resolution differences\, tissue deformation\, artifacts\, and impreci
se correspondence. We propose a novel deep learning-based pipeline for aff
ine registration of faxitron images (x-ray representations of macrosection
s of ex-vivo breast tissue) with their corresponding histopathology images
. Our model combines convolutional neural networks (CNN) and vision transf
ormers (ViT)\, capturing local and global information from the entire tiss
ue macrosection and its segments. This integrated approach enables simulta
neous registration and stitching of image segments\, facilitating segment-
to-macrosection registration through a puzzling-based mechanism. To overco
me the limitations of multi-modal ground truth data\, we train the model u
sing synthetic mono-modal data in a weakly supervised manner. The trained
model successfully performs multi-modal registration\, outperforms existin
g baselines\, including deep learning-based and iterative models\, and is
approximately 200 times faster than the iterative approach. The applicatio
n of proposed registration method allows for the precise mapping of pathol
ogy labels onto radiology images\, thereby establishing ground truth label
s for training classification and detection models on radiological data. T
his work bridges the gap in current research and clinical workflow\, offer
ing potential improvements in efficiency and accuracy for breast cancer ev
aluation and streamlining pathology workflow.\n \nJean Benoit Delbrouck\,
PhD\nResearch Scientist\nDepartment of Radiology\nStanford University\n\nT
itle: Generating Accurate and Factually Correct Medical Text\nAbstract: Ge
nerating factually correct medical text is of utmost importance due to sev
eral reasons. Firstly\, patient safety is heavily dependent on accurate in
formation as medical decisions are often made based on the information pro
vided. Secondly\, trust in AI as a reliable tool in the medical field is e
ssential\, and this trust can only be established by generating accurate a
nd reliable medical text. Lastly\, medical research also relies heavily on
accurate information for meaningful results.\nRecent studies have explore
d new approaches for generating medical text from images or findings\, ran
ging from pretraining to Reinforcement Learning\, and leveraging expert an
notations. However\, a potential game changer in the field is the integrat
ion of GPT models in pipelines for generating factually correct medical te
xt for research or production purposes.
DTSTART;TZID=America/Los_Angeles:20230927T140000
DTEND;TZID=America/Los_Angeles:20230927T150000
LOCATION:Li Ka Shing\, LK120 - Zoom Details on IBIIS website
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Negar Golestani\, PhD & Jean Benoit Delbrouck
\, PhD
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-negar-golestani-phd-jean-benoit-delbrouck-phd/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/09/NegarGolestani.png\;200\;200\,medium\;
http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/upload
s/2023/09/NegarGolestani.png\;200\;200\,large\;http://web.stanford.edu/gro
up/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/09/NegarGolestani.pn
g\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/radcalenda
r/wp-content/uploads/2023/09/NegarGolestani.png\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Negar Golestani\, PhD
\nPostdoctoral Research Fellow \nDepartment of Radiolo
gy \nStanford University \n
\n
Title: AI in Radiology-Pathology F
usion Towards Precise Breast Cancer Detection
\n
Abstract: Breast cancer is a glob
al public health concern with various treatment options based on tumor cha
racteristics. Pathological examination of excised tissue after surgery pro
vides important information for treatment decisions. This pathology proces
sing involving the manual selection of representative sections for histolo
gical examination is time-consuming and subjective\, which can lead to pot
ential sampling errors. Accurately identifying residual tumors is a challe
nging task\, which highlights the need for systematic or assisted methods.
Radiology-pathology registration is essential for developing deep-learnin
g algorithms to automate cancer detection on radiology images. However\, a
ligning faxitron and histopathology images is difficult due to content and
resolution differences\, tissue deformation\, artifacts\, and imprecise c
orrespondence. We propose a novel deep learning-based pipeline for affine
registration of faxitron images (x-ray representations of macrosections of
ex-vivo breast tissue) with their corresponding histopathology images. Ou
r model combines convolutional neural networks (CNN) and vision transforme
rs (ViT)\, capturing local and global information from the entire tissue m
acrosection and its segments. This integrated approach enables simultaneou
s registration and stitching of image segments\, facilitating segment-to-m
acrosection registration through a puzzling-based mechanism. To overcome t
he limitations of multi-modal ground truth data\, we train the model using
synthetic mono-modal data in a weakly supervised manner. The trained mode
l successfully performs multi-modal registration\, outperforms existing ba
selines\, including deep learning-based and iterative models\, and is appr
oximately 200 times faster than the iterative approach. The application of
proposed registration method allows for the precise mapping of pathology
labels onto radiology images\, thereby establishing ground truth labels fo
r training classification and detection models on radiological data. This
work bridges the gap in current research and clinical workflow\, offering
potential improvements in efficiency and accuracy for breast cancer evalua
tion and streamlining pathology workflow.
\n
\n
Jean Benoit Delbrouck\, PhD \nResearch Scientist \n
Department of Radiology \nStanford University \n<
/strong>
\n
Title: Genera
ting Accurate and Factually Correct Medical Text \nAbstract:<
/strong> Generating factually correct medical text is of utmost importance
due to several reasons. Firstly\, patient safety is heavily dependent on
accurate information as medical decisions are often made based on the info
rmation provided. Secondly\, trust in AI as a reliable tool in the medical
field is essential\, and this trust can only be established by generating
accurate and reliable medical text. Lastly\, medical research also relies
heavily on accurate information for meaningful results.
\n
Recent studies have explored new approaches for generati
ng medical text from images or findings\, ranging from pretraining to Rein
forcement Learning\, and leveraging expert annotations. However\, a potent
ial game changer in the field is the integration of GPT models in pipeline
s for generating factually correct medical text for research or production
purposes.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3166@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 16507214161\; rtotah@stanford.edu\; https://ibiis.sta
nford.edu/events/seminars/2023seminars.html
DESCRIPTION:Bram van Ginneken\, PhD\nProfessor of Medical Image Analysis\nC
hair of the Diagnostic Image Analysis Group\nRadboud University Medical Ce
nter\nTitle: Why AI Should Replace Radiologists\nAbstract:\nIn this talk\,
I will provide arguments for the thesis that nearly all diagnostic radiol
ogy could be performed by computers and that the notion that AI will not r
eplace radiologists is only temporarily true. Some well-known and lesser-k
nown examples of AI systems analyzing medical images with a stand-alone pe
rformance on par or beyond human experts will be presented. I will show th
at systems built by academia\, in collaborative efforts\, may even outperf
orm commercially available systems. Next\, I will sketch a way forward to
implement automated diagnostic radiology and argue that this is needed to
keep healthcare affordable in societies wrestling with aging populations.
Some pitfalls\, like excessive demands for trials\, will be discussed. Th
e key to success is to convince radiologists to take the lead in this proc
ess. They need to collaborate with AI developers\, but AI developers and t
he medical device industry should not lead this process. Radiologists shou
ld\, in fact\, stop training radiologists\, and instead\, start training m
achines.
DTSTART;TZID=America/Los_Angeles:20231115T090000
DTEND;TZID=America/Los_Angeles:20231115T100000
LOCATION:ZOOM: https://stanford.zoom.us/j/97076943141?pwd=Z2E5eGtaUDdNVklEY
VNpcDJzcy9sdz09
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar: Why AI Should Replace Radiologists
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-why-ai-should-replace-radiologists/
X-COST-TYPE:free
X-WP-IMAGES-URL:thumbnail\;http://web.stanford.edu/group/radweb/cgi-bin/rad
calendar/wp-content/uploads/2023/11/Bram_van_Ginneken.jpg\;200\;200\,mediu
m\;http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/upl
oads/2023/11/Bram_van_Ginneken.jpg\;200\;200\,large\;http://web.stanford.e
du/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2023/11/Bram_van_Gi
nneken.jpg\;200\;200\,full\;http://web.stanford.edu/group/radweb/cgi-bin/r
adcalendar/wp-content/uploads/2023/11/Bram_van_Ginneken.jpg\;200\;200
X-ALT-DESC;FMTTYPE=text/html:\\n\\n\\n\\n\\n\n
Bram van Ginneken\, PhD \nPr
ofessor of Medical Image Analysis \nChair of the Diagnostic Image Ana
lysis Group \nRadboud University Medical Center
\n
Title:
Why AI Should Replace Radiologists
\n
Abstract: \nIn thi
s talk\, I will provide arguments for the thesis that nearly all diagnosti
c radiology could be performed by computers and that the notion that AI wi
ll not replace radiologists is only temporarily true. Some well-known and
lesser-known examples of AI systems analyzing medical images with a stand-
alone performance on par or beyond human experts will be presented. I will
show that systems built by academia\, in collaborative efforts\, may even
outperform commercially available systems. Next\, I will sketch a way for
ward to implement automated diagnostic radiology and argue that this is n
eeded to keep healthcare affordable in societies wrestling with aging popu
lations. Some pitfalls\, like excessive demands for trials\, will be discu
ssed. The key to success is to convince radiologists to take the lead in t
his process. They need to collaborate with AI developers\, but AI develope
rs and the medical device industry should not lead this process. Radiologi
sts should\, in fact\, stop training radiologists\, and instead\, start tr
aining machines.
\n
END:VEVENT
BEGIN:VEVENT
UID:ai1ec-3170@web.stanford.edu/group/radweb/cgi-bin/radcalendar
DTSTAMP:20240329T155838Z
CATEGORIES;LANGUAGE=en-US:AIMI
CONTACT:Ramzi Totah\; 6507214161\; rtotah@stanford.edu\; https://ibiis.stan
ford.edu/events/seminars/2023seminars.html
DESCRIPTION:Andrey Fedorov\, PhD \nAssociate Professor\, Harvard Medical Sc
hool\nLead Investigator\, Brigham and Women’s Hospital\n\n\n\nTitle: NCI I
maging Data Commons:Towards Transparency\, Reproducibility\, and Scalabili
ty in Imaging AI\n\n\n\n\n\n\n\n\n\nAbstract\nThe remarkable advances of a
rtificial intelligence (AI) technology are revolutionizing established app
roaches to the acquisition\, interpretation\, and analysis of biomedical i
maging data. Development\, validation\, and continuous refinement of AI to
ols requires easy access to large high-quality annotated datasets\, which
are both representative and diverse. The National Cancer Institute (NCI)
Imaging Data Commons (IDC) hosts over 50 TB of diverse publicly available
cancer image data spanning radiology and microscopy domains. By harmonizin
g all data based on industry standards and colocalizing it with analysis
and exploration resources\, IDC aims to facilitate the development\, valid
ation\, and clinical translation of AI tools and address the well-document
ed challenges of establishing reproducible and transparent AI processing
pipelines. Balanced use of established commercial products with open-sourc
e solutions\, interconnected by standard interfaces\, provides value and
performance\, while preserving sufficient agility to address the evolving
needs of the research community. Emphasis on the development of tools\, us
e cases to demonstrate the utility of uniform data representation\, and c
loud-based analysis aim to ease adoption and help define best practices. I
ntegration with other data in the broader NCI Cancer Research Data Commons
infrastructure opens opportunities for multiomics studies incorporating i
maging data to further empower the research community to accelerate breakt
hroughs in cancer detection\, diagnosis\, and treatment. The presentation
will discuss the recent developments in IDC\, highlighting resources\, dem
onstrations and examples that we hope can help you improve your everyday i
maging research practices – both those that use public and internal datase
ts.
DTSTART;TZID=America/Los_Angeles:20240320T120000
DTEND;TZID=America/Los_Angeles:20240320T130000
LOCATION:Clark Center S360 - Zoom Details on IBIIS website @ 318 Campus Dri
ve
SEQUENCE:0
SUMMARY:IBIIS & AIMI Seminar – NCI Imaging Data Commons: Towards Transparen
cy\, Reproducibility\, and Scalability in Imaging AI
URL:http://web.stanford.edu/group/radweb/cgi-bin/radcalendar/event/ibiis-ai
mi-seminar-nci-imaging-data-commons-towards-transparency-reproducibility-a
nd-scalability-in-imaging-ai/
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Andrey Fedorov\, PhD \nAssociate Professor\
, Harvard Medical School \nLead Investigator\, Brigham and Women’s Ho
spital
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Titl
e: NCI Imaging Data Commons:Towards Transparency\, Reproducibility\, and S
calability in Imaging AI
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Abstract \nThe remarkable advances of artifi
cial intelligence (AI) technology are revolutionizing established approach
es to the acquisition\, interpretation\, and analysis of biomedical imagin
g data. Development\, validation\, and continuous refinement of AI tools r
equires easy access to large high-quality annotated datasets\, which are
both representative and diverse. The National Cancer Institute (NCI) Imagi
ng Data Commons (IDC) hosts over 50 TB of diverse publicly available cance
r image data spanning radiology and microscopy domains. By harmonizing all
data based on industry standards and colocalizing it with analysis and e
xploration resources\, IDC aims to facilitate the development\, validation
\, and clinical translation of AI tools and address the well-documented ch
allenges of establishing reproducible and transparent AI processing pipel
ines. Balanced use of established commercial products with open-source sol
utions\, interconnected by standard interfaces\, provides value and perfo
rmance\, while preserving sufficient agility to address the evolving needs
of the research community. Emphasis on the development of tools\, use cas
es to demonstrate the utility of uniform data representation\, and cloud-
based analysis aim to ease adoption and help define best practices. Integr
ation with other data in the broader NCI Cancer Research Data Commons infr
astructure opens opportunities for multiomics studies incorporating imagin
g data to further empower the research community to accelerate breakthroug
hs in cancer detection\, diagnosis\, and treatment. The presentation will
discuss the recent developments in IDC\, highlighting resources\, demonstr
ations and examples that we hope can help you improve your everyday imagin
g research practices – both those that use public and internal datasets.
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