Calendar

Sep
27
Mon
2021
2021 IBIIS & AIMI Virtual Retreat @ https://ibiis.stanford.edu/events/retreat/2021Hybrid.html
Sep 27 @ 1:00 pm – 4:30 pm

Keynote:

Self-Supervision for Learning from the Bottom Up

Why do self-supervised learning? A common answer is: “because data labeling is expensive.” In this talk, I will argue that there are other, perhaps more 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 datasets and enable continuous, online learning, which is a much more natural setting for real-world agents. Third, and most intriguingly, there is hope that it might be possible to force a self-supervised task curriculum 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 touch upon these themes to argue that, far from running its course, research in self-supervised learning is only just beginning.

Oct
6
Wed
2021
Early Detection of Cancer Conference @ Virtual Event
Oct 6 – Oct 8 all-day
Early Detection of Cancer Conference @ Virtual Event

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 research and progress in the field.

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 methods, and enhance uptake and accuracy of screening.

The 2021 conference will take place October 6-8 virtuallyFor more information visit the website: http://earlydetectionresearch.com/

Oct
8
Fri
2021
CME Grand Rounds – Christoph L. Lee, MD, MS, MBA @ Zoom - See Description for Zoom Link
Oct 8 @ 12:00 pm – 1:00 pm
CME Grand Rounds - Christoph L. Lee, MD, MS, MBA @ Zoom - See Description for Zoom Link

CME Grand Rounds – “Community Based Partnered Research:  Revisiting a Critical Concept for Radiology”

 

Christoph L. Lee, MD, MS, MBA
Professor
Radiology
University of Washington

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Oct
12
Tue
2021
Cancer Early Detection Seminar Series – Azra Raza, MD @ Venue coming soon!
Oct 12 @ 11:00 am – 12:00 pm
Cancer Early Detection Seminar Series - Azra Raza, MD @ Venue coming soon!

CEDSS: The First Cell: A new model for cancer research and treatment

Azra Raza, M.D.
Chan Soon-Shiong Professor of Medicine
Director, Myelodysplastic Syndrome Center
Columbia University Medical Center

 

Location: Zoom
Meeting URL: https://stanford.zoom.us/s/99340345860
Dial: US: +1 650 724 9799  or +1 833 302 1536 (Toll Free)
Meeting ID: 993 4034 5860
Passcode: 711508

RSVP Here

 

ABSTRACT

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 diagnose 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 study 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 blood 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 studying the serial samples of patients who evolve from MDS to AML.

 

ABOUT

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 Charles 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 successfully relocated to the University of Massachusetts in 2004 and to Columbia University in 2010.

Before moving to New York, Dr. Raza was the Chief of Hematology Oncology and the Gladys Smith Martin Professor of Oncology at the University of Massachussetts in Worcester. She has published the results of her laboratory research and clinical trials in prestigious, peer reviewed journals such as The New England Journal of Medicine, Nature, Blood, Cancer, Cancer Research, British Journal of Hematology, Leukemia, and Leukemia Research. Dr. Raza serves on numerous national and international panels as a reviewer, consultant and advisor and is the recipient of a number of awards.

 

Hosted by: Utkan Demirci, Ph.D.
Spon
sored by: The Canary Center & the Department of Radiology 
Stanford University – School of Medicine

Oct
22
Fri
2021
CME Grand Rounds – Jocelyn D. Chertoff, MD, MS @ Zoom - See Description for Zoom Link
Oct 22 @ 12:00 pm – 1:00 pm
CME Grand Rounds - Jocelyn D. Chertoff, MD, MS @ Zoom - See Description for Zoom Link

CME Grand Rounds – Topic: TBD

Jocelyn D. Chertoff, MD, MS
Professor
Radiology, Obstetrics & Gynecology
Chair, Radiology
Dartmouth Hitchcock Medical Center

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Nov
4
Thu
2021
CME Grand Rounds Etta K. Moskowitz Lectureship – Elizabeth Krupinski, PhD @ Zoom - See Description for Zoom Link
Nov 4 @ 5:30 pm – 6:30 pm
CME Grand Rounds Etta K. Moskowitz Lectureship - Elizabeth Krupinski, PhD @ Zoom - See Description for Zoom Link

CME Grand Rounds Etta K. Moskowitz Lectureship – Topic: TBD

Elizabeth Krupinski, PhD
Professor & Vice Chair for Research
Radiology & Imaging Sciences
Emory University School of Medicine

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Nov
12
Fri
2021
CME Grand Rounds – Michael Gisondi, MD @ Zoom - See Description for Zoom Link
Nov 12 @ 12:00 pm – 1:00 pm
CME Grand Rounds - Michael Gisondi, MD @ Zoom - See Description for Zoom Link

CME Grand Rounds – “Promote Your Academic Career Using Social Media”

Michael Gisondi, MD
Associate Professor & Vice Chair of Education
Emergency Medicine
Stanford University

 

Join from PC, Mac, Linux, iOS or Android: https://stanford.zoom.us/j/600003703?pwd=RjcwS2MvOG1qVkxyL3U0RmNtUDVWdz09
Meeting ID: 600 003 703
Password: 566048
Or iPhone one-tap (US Toll): +18333021536,,600003703# or +16507249799,,600003703#
Or Telephone:
Dial: +1 650 724 9799 (US, Canada, Caribbean Toll) or +1 833 302 1536 (US, Canada, Caribbean Toll Free)
International numbers available: https://stanford.zoom.us/u/acuqphnvqT

 

ABSTRACT
Coming soon!

 

BIO
Coming soon!

Nov
17
Wed
2021
IBIIS & AIMI Seminar: Deep Learning for Histology Images Analysis @ Zoom: https://stanford.zoom.us/j/91788140120?pwd=K2NvMHZ2SUFVWjc1d2xJUndjTG9lQT09
Nov 17 @ 12:00 pm – 1:00 pm

Saeed Hassanpour, PhD
Associate Professor of Biomedical Data Science
Associate Professor of Epidemiology
Associate Professor of Computer Science
Dartmouth Geisel School of Medicine

Deep Learning for Histology Images Analysis

Abstract:
With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit significantly from deep learning technology. This talk will cover several applications of deep learning for characterizing histopathological patterns on high-resolution microscopy 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 bottlenecks will be presented.

About:

Dr. Saeed Hassanpour is an Associate Professor 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 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 worked as a Research Engineer at Microsoft. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and completed his postdoctoral training at Stanford Center for Artificial Intelligence in Medicine & Imaging.

Dec
15
Wed
2021
IBIIS & AIMI Seminar: Indrani Bhattacharya, PhD & Rogier van der Sluijs, PhD @ Zoom: https://stanford.zoom.us/j/95371438521?pwd=Y3BheHpUanpESnh6VUkycVhlUWtodz09
Dec 15 @ 12:00 pm – 1:00 pm

Indrani Bhattacharya, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University

Title: Multimodal Data Fusion for Selective Identification of Aggressive and Indolent Prostate Cancer on Magnetic Resonance Imaging

Abstract: Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist in early diagnosis and treatment planning. Existing automated methods of prostate cancer detection mostly rely on ground truth labels with limited accuracy, ignore disease pathology characteristics observed on resected tissue, and cannot selectively identify aggressive (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 selectively identify and localize aggressive and indolent cancers on prostate MRI.

Rogier van der Sluijs, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University

Title: Pretraining Neural Networks for Medical AI

Abstract: Transfer learning has quickly become standard practice for deep learning on medical images. Typically, practitioners repurpose existing neural networks and their corresponding weights to bootstrap 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 that address these bottlenecks will be highlighted.

Jan
19
Wed
2022
IBIIS & AIMI Seminar: AI In Clinical Use – Lessons Learned @ Zoom: https://stanford.zoom.us/j/92632628279?pwd=S3RFdXdEUmEweTNKelhrcmVxQUExdz09
Jan 19 @ 12:00 pm – 1:00 pm

Nina Kottler, MD, MS
Associate Chief Medical Officer, Clinical AI
VP Clinical Operations
Radiology Partners

Abstract:
We have a call to action in healthcare – we need to drive value.  Artificial intelligence (AI), if deployed correctly, can help accomplish this lofty mission.  In this discussion we will review the following lessons learned in deploying radiology AI at scale:  4 unexpected benefits of implementing AI emergent finding triage; the importance of investing in AI radiologist education; 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 platform 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 look like in a tech enabled future).

Bio:
Dr. Kottler has been a practicing radiologist specializing in emergency imaging for over 16 years.  Combining 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 held multiple leadership positions within her practice 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, is a member of the diversity and inclusion committee at SIIM, serves on the steering committee for RAD=, and leads the education and development division of the Belonging Committee within Radiology Partners.