About

Overview

The rapid growth of foundation models in various domains has been transformative, bringing unprecedented capabilities and advances in automated understanding. Medical vision, a pivotal segment of computer vision, is poised to greatly benefit from these advancements. This workshop delves into the integration and application of foundation models specific to the realm of medical imaging. We will cover state-of-the-art techniques for diverse medical data, such as echocardiogram, fundus, pathology, and radiology, as well as the practical challenges of implementing these models in clinical settings. Through expert-led sessions, interactive discussions, and international competitions, we aim to offer attendees a comprehensive understanding of the potential impact foundation models could have on the future of medical diagnostics and patient care.

Schedule (Room Summit 324)

Pacific Time

Time (06/17) Speaker
8:30 - 8:40 Welcome and Opening Remarks
8:40 - 9:20 Shekoofeh Azizi
Emergence of Foundation Models: Opportunities to Rethink Medical AI
9:20 - 10:00 Sharon Xiaolei Huang
Foundation Models for Medical Image and Video Generation
10:00 - 10:40 Coffee Break
10:40 - 11:20 Mu Wei
BiomedParse: A Biomedical Foundation Model for Image Parsing of Everything Everywhere All At Once
11:20 - 12:00 Faisal Mahmood
Multimodal and Generative AI for Pathology
12:00 - 13:40 Lunch Break
13:40 - 14:20 David Ouyang
Development to Deployment of Cardiovascular AI
14:20 - 15:00 Hoifung Poon
Multimodal Generative AI: the Next Frontier in Precision Health
15:00 - 15:40 Coffee Break
15:40 - 18:00 Competition Winner Presentations

Speakers

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Shekoofeh Azizi

Google

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Sharon Xiaolei Huang

Penn State University

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Mu Wei

Microsoft Research

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Faisal Mahmood

Harvard Medical School

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David Ouyang

Cedars-Sinai Medical Center

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Hoifung Poon

Microsoft

Organizers

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Jun Ma

University of Toronto, University Health Network, Vector Institute

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Vishal M. Patel

Johns Hopkins University

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Julia A Schnabel

Technical University of Munich

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Yuyin Zhou

University of California, Santa Cruz

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Bo Wang

University of Toronto, University Health Network, Vector Institute