Calendar

Dec
11
Wed
2019
AIMI, IBIIS & RSL Special Seminar - John Stafford & Bjorn Carey @ Clark Center - S360
AIMI, IBIIS & RSL Special Seminar – John Stafford & Bjorn Carey
Dec 11 @ 10:00 am – 11:00 am Clark Center - S360
AIMI, IBIIS & RSL Special Seminar - John Stafford & Bjorn Carey @ Clark Center - S360

“Messaging in the Age of Microtargeting”

John Stafford
Assistant Vice President
Digital Strategy
Stanford University

Bjorn Carey
Senior Director
Digital Strategy
Stanford University

Join via Zoom: https://stanford.zoom.us/j/400566542

Abstract:
Communications has become increasingly data-driven, targeted, and personalized. This has changed how Stanford analyzes communications opportunities from a research perspective and how it engages with relevant audiences. In this presentation, John and Bjorn will share the data and communications strategy underlying three communications initiatives and the resulting execution. They will also provide practical advice for individual thought leadership and communications in this dynamic environment.

About:
John Stafford, MA ’06, is currently Assistant Vice President for Digital Strategy at Stanford, the most senior digital communications role in the university. John is responsible for all aspects of creating a world-class digital communications function: setting the group’s strategy, building analytics and insight programs, counseling on crisis communications, leading multi-channel messaging initiatives, and advising colleagues across the University. He received a Master’s Degree in Communication from Stanford, a B.A. in History from the University of San Francisco, and was a founding advisor to Stanford Medicine X.

Refreshments will be provided.

AIMI & IBIIS Seminar - Luciano M. Prevedello, MD, MPH @ Clark Center - S360
AIMI & IBIIS Seminar – Luciano M. Prevedello, MD, MPH
Dec 11 @ 2:00 pm – 3:00 pm Clark Center - S360
AIMI & IBIIS Seminar - Luciano M. Prevedello, MD, MPH @ Clark Center - S360

“Algorithm Development Lifecycle in Medical Imaging:
Current State and Considerations for the Future”

Luciano M. Prevedello, MD, MPH
Vice-Chair for Medical Informatics and Augmented Intelligence in Imaging
Division Chief, Medical Imaging Informatics
Director, 3D and Advanced Visualization Lab
Associate Professor, Division of Neuroradiology,
Department of Radiology
Ohio State University Wexner Medical Center

Join via Zoom: https://stanford.zoom.us/j/267814863

Abstract:
This presentation will describe some of the most important considerations involved in creating algorithms in medical imaging from inception to deployment as well as continued model improvement and/or monitoring. Examples of experience to date from the OSU laboratory for augmented intelligence in imaging will be provided. New paradigms in model creation and the role of image challenge competitions will also be covered. Current issues with model validation and generalizability will also be introduced as well as considerations for future work in this area.

Refreshments will be provided.

Jan
15
Wed
2020
AIMI & IBIIS Seminar - Wei Shao, PhD & Saeed Seyyedi, PhD @ Clark Center - S360
AIMI & IBIIS Seminar – Wei Shao, PhD & Saeed Seyyedi, PhD
Jan 15 @ 12:00 pm – 1:00 pm Clark Center - S360
AIMI & IBIIS Seminar - Wei Shao, PhD & Saeed Seyyedi, PhD @ Clark Center - S360

“A Deep Learning Framework for Efficient Registration of MRI and Histopathology Images of the Prostate”

Wei Shao, PhD
Postdoctoral Research Fellow
Department of Radiology
Stanford University

“Applications of Generative Adversarial Networks (GANs) in Medical Imaging”

Saeed Seyyedi, PhD
Paustenbach Research Fellow
Department of Radiology
Stanford University

Join via Zoom: https://stanford.zoom.us/j/593016899

Refreshments will be provided

ABSTRACT (Shao)
Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, MRI interpretation suffers from high interobserver variability and often misses clinically significant cancers. Registration of histopathology images from patients who have undergone surgical resection of the prostate onto pre-operative MRI images allows direct mapping of cancer location onto MR images. This is essential for the discovery and validation of novel prostate cancer signatures on MRI. Traditional registration approaches can be computationally expensive and require a careful choice of registration hyperparameters. We present a deep learning-based pipeline to accelerate and simplify MRI-histopathology image registration in prostate cancer. Our pipeline consists of preprocessing, transform estimation by deep neural networks, and postprocessing. We refined the registration neural networks, originally trained with 19,642 natural images, by adding 17,821 medical images of the prostate to the training set. The pipeline was evaluated using 99 prostate cancer patients. The addition of the images to the training set significantly (p < 0.001) improved the Dice coefficient and reduced the Hausdorff distance. Our pipeline also achieved comparable accuracy to an existing state-of-the-art algorithm while reducing the computation time from 4.4 minutes to less than 2 seconds.

ABSTRACT (Seyyedi)
Generative adversarial networks (GANs) are advanced types of neural networks where two networks are trained simultaneously to perform two tasks of generation and discrimination. GANs have gained a lot of attention to tackle well known and challenging problems in computer vision applications including medical image analysis tasks such as medical image de-noising, detection and classification, segmentation and reconstruction.In this talk, we will introduce some of the recent advancements of GANs in medical imaging applications and will discuss the recent developments of GAN models to resolve real world imaging challenges.

Feb
13
Thu
2020
Evolving Health Care from an Artisanal Organization into an Industrial Enterprise @ Clark Center, S361
Evolving Health Care from an Artisanal Organization into an Industrial Enterprise
Feb 13 @ 12:30 pm – 1:30 pm Clark Center, S361
Evolving Health Care from an Artisanal Organization into an Industrial Enterprise @ Clark Center, S361

Ron Kikinis, MD
Director of the Surgical Planning Laboratory
Professor of Radiology
Department of Radiology
Brigham and Women’s Hospital
Harvard Medical School

Title: Evolving Health Care from an Artisanal Organization into an Industrial Enterprise

Refreshments will be provided

Join via Zoom: https://stanford.zoom.us/j/996417088

Abstract: During the last decade, results from basic research in the fields of genetics and immunology have begun to impact treatment in a variety of diseases. Checkpoint therapy, for instance has fundamentally changed the treatment and survival of some patients with melanoma. The medical workplace has transformed from an artisanal organization into an industrial enterprise environment. Workflows in the clinic are increasingly standardized. Their timing and execution are monitored through omnipresent software systems. This has resulted in an acceleration of the pace of care delivery. Imaging and image post-processing have rapidly evolved as well, enabled by ever-increasing computational power, novel sensor systems and novel mathematical approaches. Organizing the data and making it findable and accessible is an ongoing challenge and is investigated through a variety of research efforts. These topics will be reviewed and discussed during the lecture.

About:

Dr. Kikinis is the founding Director of the Surgical Planning Laboratory, Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, and a Professor of Radiology at Harvard Medical School. This laboratory was founded in 1990. Before joining Brigham & Women’s Hospital in 1988, he trained as a resident in radiology at the University Hospital in Zurich, and as a researcher in computer vision at the ETH in Zurich, Switzerland. He received his M.D. degree from the University of Zurich, Switzerland, in 1982. In 2004 he was appointed Professor of Radiology at Harvard Medical School. In 2009 he was the inaugural recipient of the MICCAI Society “Enduring Impact Award”. On February 24, 2010 he was appointed the Robert Greenes Distinguished Director of Biomedical Informatics in the Department of Radiology at Brigham and Women’s Hospital. On January 1, 2014, he was appointed “Institutsleiter” of Fraunhofer MEVIS and Professor of Medical Image Computing at the University of Bremen. Since then he is commuting every two months between Bremen and Boston.

During the mid-80’s, Dr. Kikinis developed a scientific interest in image processing algorithms and their use for extracting relevant information from medical imaging data. Due to the explosive increase of both the quantity and complexity of imaging data this area of research is of ever-increasing importance. Dr. Kikinis has led and has participated in research in different areas of science. His activities include technological research (segmentation, registration, visualization, high performance computing), software system development, and biomedical research in a variety of biomedical specialties. The majority of his research is interdisciplinary in nature and is conducted by multidisciplinary teams. The results of his research have been reported in a variety of peer-reviewed journal articles. He is an author and co-author of over 350 peer-reviewed articles.

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http://ibiis.stanford.edu/events/seminars/2020seminars.html

Feb
19
Wed
2020
Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples @ Clark Center S360
Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples
Feb 19 @ 2:00 pm – 3:00 pm Clark Center S360
Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples @ Clark Center S360

Tessa Cook, MD, PhD
Assistant Professor of Radiology
Perelman School of Medicine
University of Pennsylvania

Title: Deploying AI in the Clinical Radiology Workflow: Challenges, Opportunities, and Examples

Abstract: Although many radiology AI efforts are focused on pixel-based tasks, there is great potential for AI to impact radiology care delivery and workflow when applied to reports, EMR data, and workflow data. Radiology-pathology correlation, identification of follow-up recommendations, and report segmentation can be used to increase meaningful feedback to radiologists as well as to automate tasks that are currently manual and time-consuming. When deploying AI within the clinical workflow, there are many challenges that may slow down or otherwise affect the integration. Careful consideration of the way in which radiologists may expect to interact with AI results should be undertaken to meaningfully deploy radiology AI in a safe and effective way.

Apr
22
Wed
2020
IBIIS/AIMI Seminar - Tiwari @ ZOOM - See Description for Zoom link
IBIIS/AIMI Seminar – Tiwari
Apr 22 @ 1:00 pm – 2:00 pm ZOOM - See Description for Zoom link
IBIIS/AIMI Seminar - Tiwari @ ZOOM - See Description for Zoom link

Radiomics and Radio-Genomics: Opportunities for Precision Medicine

Zoom: https://stanford.zoom.us/j/99904033216?pwd=U2tTdUp0YWtneTNUb1E4V2x0OTFMQT09 

Pallavi Tiwari, PhD
Assistant Professor of Biomedical Engineering
Associate Member, Case Comprehensive Cancer Center
Director of Brain Image Computing Laboratory
School of Medicine | Case Western Reserve University


Abstract:
In this talk, Dr. Tiwari will focus on her lab’s recent efforts in developing radiomic (extracting computerized sub-visual features from radiologic imaging), radiogenomic (identifying radiologic features associated with molecular phenotypes), and radiopathomic (radiologic features associated with pathologic phenotypes) techniques to capture insights into the underlying tumor biology as observed on non-invasive routine imaging. She will focus on clinical applications of this work for predicting disease outcome, recurrence, progression and response to therapy specifically in the context of brain tumors. She will also discuss current efforts in developing new radiomic features for post-treatment evaluation and predicting response to chemo-radiation treatment. Dr. Tiwari will conclude with a discussion on her lab’s findings in AI + experts, in the context of a clinically challenging problem of post-treatment response assessment on routine MRI scans.

Nov
18
Wed
2020
IBIIS & AIMI Seminar: Deep Tomographic Imaging @ Zoom: https://stanford.zoom.us/j/96731559276?pwd=WG5zcEFwSGlPcDRsOUFkVlRhcEs2Zz09
IBIIS & AIMI Seminar: Deep Tomographic Imaging
Nov 18 @ 12:00 pm – 1:00 pm Zoom: https://stanford.zoom.us/j/96731559276?pwd=WG5zcEFwSGlPcDRsOUFkVlRhcEs2Zz09

Ge Wang, PhD
Clark & Crossan Endowed Chair Professor
Director of the Biomedical Imaging Center
Rensselaer Polytechnic Institute
Troy, New York

Abstract:
AI-based tomography is an important application and a new frontier of machine learning. AI, especially deep learning, has been widely used in computer vision and image analysis, which deal with existing images, improve them, and produce features. Since 2016, deep learning techniques are actively researched for tomography in the context of medicine. Tomographic reconstruction produces images of multi-dimensional structures from externally measured “encoded” data in the form of various transforms (integrals, harmonics, and so on). In this presentation, we provide a general background, highlight representative results, and discuss key issues that need to be addressed in this emerging field.

About:
AI-based X-ray Imaging System (AXIS) lab is led by Dr. Ge Wang, affiliated with the Department of Biomedical Engineering at Rensselaer Polytechnic Institute and the Center for Biotechnology and Interdisciplinary Studies in the Biomedical Imaging Center. AXIS lab focuses on innovation and translation of x-ray computed tomography, optical molecular tomography, multi-scale and multi-modality imaging, and AI/machine learning for image reconstruction and analysis, and has been continuously well funded by federal agencies and leading companies. AXIS group collaborates with Stanford, Harvard, Cornell, MSK, UTSW, Yale, GE, Hologic, and others, to develop theories, methods, software, systems, applications, and workflows.

Sep
10
Fri
2021
CME Grand Rounds Sanjiv Sam Gambhir Lectureship - Simon Cherry, PhD @ LKSC 101/102 & Zoom - See Description for Zoom Link
CME Grand Rounds Sanjiv Sam Gambhir Lectureship – Simon Cherry, PhD
Sep 10 @ 12:00 pm – 1:00 pm LKSC 101/102 & Zoom - See Description for Zoom Link
CME Grand Rounds Sanjiv Sam Gambhir Lectureship - Simon Cherry, PhD @ LKSC 101/102 & Zoom - See Description for Zoom Link

CME Grand Rounds Sanjiv Sam Gambhir Lectureship – “Imaging at the Speed of Light:  Innovations in Positron Emission Tomography”

 

Simon R. Cherry, PhD
Professor
Biomedical Engineering & Radiology
UC Davis

 

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

Positron emission tomography (PET) allows for sensitive and quantitative measurement of physiology, metabolism and molecular targets noninvasively in the human body.  However, typical clinical PET scanners capture less than 1% of the available signal produced in the body.  PET scanners also are not currently capable of precisely determining the location at which a particular decay occurs. These limitations present opportunities for further innovation that ultimately will impact molecular imaging research and diagnostic imaging with PET.  This presentation focuses on 1) total-body PET imaging which greatly improves signal collection, allowing radiotracer kinetics to be assessed across the entire human body for the first time, and 2) the development of detector technologies that have a timing precision of ~ 30 picoseconds, enabling direct localization of radiotracer decays without tomographic reconstruction.

 

BIO

Simon R. Cherry, Ph.D.  received his B.Sc.(Hons) in Physics with Astronomy from University College London in 1986 and a Ph.D. in Medical Physics from the Institute of Cancer Research, University of London in 1989.  After a postdoctoral fellowship at UCLA, he joined the faculty in the Department of Molecular and Medical Pharmacology, also at UCLA, in 1993. In 2001, Dr. Cherry joined UC Davis and established the Center for Molecular and Genomic Imaging, which he directed from 2004-2016. Currently Dr. Cherry is Distinguished Professor in the Departments of Biomedical Engineering and Radiology at UC Davis.

Dr. Cherry’s research interests center around biomedical imaging and in particular the development and application of in vivo molecular imaging systems.  His major accomplishments have been in developing systems for positron emission tomography (PET), in particular the invention of the microPET technology that was subsequently widely adopted in academia and industry and as co-leader of the EXPLORER consortium which has developed the world’s first total-body PET scanner.  He also has contributed to detector technology innovations for PET, conducted early biomedical studies using Cerenkov luminescence, and developed the first proof-of-concept hybrid PET/MRI (magnetic resonance imaging) systems.

Dr. Cherry is a founding member of the Society of Molecular Imaging and an elected fellow of six professional societies, including the Institute for Electronic and Electrical Engineers (IEEE) and the Biomedical Engineering Society (BMES). He served as Editor-in-Chief of the journal Physics in Medicine and Biology from 2011-2020. Dr. Cherry received the Academy of Molecular Imaging Distinguished Basic Scientist Award (2007), the Society for Molecular Imaging Achievement Award (2011) and the IEEE Marie Sklodowska-Curie Award (2016).   In 2016, he was elected as a member of the National Academy of Engineering and in 2017 he was elected to the National Academy of Inventors.  Dr. Cherry is the author of more than 240 peer-reviewed journal articles, review articles and book chapters in the field of biomedical imaging. He is also lead author of the widely-used textbook “Physics in Nuclear Medicine”.

Sep
24
Fri
2021
CME Grand Rounds Diversity Lectureship - Jennifer L. Eberhardt, PhD @ Zoom - See Description for Zoom Link
CME Grand Rounds Diversity Lectureship – Jennifer L. Eberhardt, PhD
Sep 24 @ 12:00 pm – 1:00 pm Zoom - See Description for Zoom Link
CME Grand Rounds Diversity Lectureship - Jennifer L. Eberhardt, PhD @ Zoom - See Description for Zoom Link

CME Grand Rounds Diversity Lectureship – Topic: TBD

 

Jennifer L. Eberhardt, PhD
Professor
Psychology
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!

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