![CME Grand Rounds Diversity Lectureship - Jennifer L. Eberhardt, PhD @ Zoom - See Description for Zoom Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/download.jpg)
CME Grand Rounds Diversity Lectureship – Topic: TBD
Jennifer L. Eberhardt, PhD
Professor
Psychology
Stanford University
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ABSTRACT
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BIO
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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.
![CME Grand Rounds - Christoph L. Lee, MD, MS, MBA @ Zoom - See Description for Zoom Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/C-Lee2017_2-214x300-214x300.jpg)
CME Grand Rounds – “Community Based Partnered Research: Revisiting a Critical Concept for Radiology”
Christoph L. Lee, MD, MS, MBA
Professor
Radiology
University of Washington
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Meeting ID: 600 003 703
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ABSTRACT
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BIO
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![CME Grand Rounds - Jocelyn D. Chertoff, MD, MS @ Zoom - See Description for Zoom Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/Chertoff.jpg)
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
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ABSTRACT
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BIO
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![MIPS Seminar - Steven Paul Poplack, MD @ Venue coming soon!](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/10/steven-poplack-300x300.jpg)
MIPS Seminar Series: Title TBA
Steven Paul Poplack, MD
Professor of Radiology (Breast Imaging)
Stanford University Medical Center
Location: Coming soon!
12:00pm – 12:45pm Seminar & Discussion
RSVP: Coming soon!
ABSTRACT
Coming soon!
ABOUT
Coming soon!
Hosted by: Katherine Ferrara, PhD
Sponsored by: Molecular Imaging Program at Stanford & the Department of Radiology
![CME Grand Rounds Etta K. Moskowitz Lectureship - Elizabeth Krupinski, PhD @ Zoom - See Description for Zoom Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/Krupinski.jpg)
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
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Meeting ID: 600 003 703
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ABSTRACT
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BIO
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![CME Grand Rounds - Michael Gisondi, MD @ Zoom - See Description for Zoom Link](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2021/07/michael-gisondi_profilephoto-300x300.jpg)
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
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International numbers available: https://stanford.zoom.us/u/acuqphnvqT
ABSTRACT
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BIO
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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.
![MIPS Seminar - Matthew Bogyo, PhD @ Venue coming soon!](https://web.stanford.edu/group/radweb/cgi-bin/radcalendar/wp-content/uploads/2019/10/BogyoHeadshotJuly2017-244x300.jpg)
MIPS Seminar Series: Title TBA
Matthew Bogyo, PhD
Professor of Pathology and of Microbiology and Immunology and, by courtesy, of Chemical and Systems Biology
Stanford University
Location: Coming soon!
12:00pm – 12:45pm Seminar & Discussion
RSVP: Coming soon!
ABSTRACT
Coming soon!
ABOUT
Dr. Bogyo received a B.Sc. degree in Chemistry from Bates College in 1993 and a Ph.D. in Biochemistry from the Massachusetts Institute of Technology in 1997. After completion of his degree he was appointed as a Faculty Fellow in the Department of Biochemistry and Biophysics at the University of California, San Francisco. Dr. Bogyo served as the Head of Chemical Proteomics at Celera Genomics from 2001 to 2003 while maintaining an Adjunct Faculty appointment at UCSF. In the Summer of 2003 Dr. Bogyo joined the Department of Pathology at Stanford Medical School and was appointed as a faculty member in the Department of Microbiology and Immunology in 2004. His interests are focused on the use of chemistry to study the role of proteases in human disease. In particular his laboratory is currently working on understanding the role of cysteine proteases in tumorgenesis and also in the life cycle of human parasites and bacterial pathogens. Dr. Bogyo currently serves on the Editorial Board of Biochemical Journal, Cell Chemical Biology, Molecular and Cellular Proteomics and is an Academic Editor at PLoS One. Dr. Bogyo is a consultant for several biotechnology and pharmaceutical companies in the Bay Area and is a founder and board member of Akrotome Imaging and Facile Therapeutics.
Hosted by: Katherine Ferrara, PhD
Sponsored by: Molecular Imaging Program at Stanford & the Department of Radiology
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.