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Talk by Prof. Shuo Li (Associate Professor, Case Western Reserve University / Founding Scientific Director, DIG of London ): AI to eliminate chemical contrast agent in imaging

2023-11-13(月)13:00 - 14:00 JST
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Date and Time: November 13th, 1:00 pm - 2:00 pm (JST)

Venue: Hybrid at
423 Room, RCAST Bldg. 4, Komba Campus, the University of Tokyo
4-6-1, Komaba, Meguro-ku, Tokyo, Japan
OR
Zoom *After this registration, you will receive a Zoom link.

Language: English

Speaker: Prof. Prof. Shuo Li (Associate Professor, Case Western Reserve University / Founding Scientific Director, DIG of London

Title: AI to eliminate chemical contrast agent in imaging

Abstract
Chemical contrast agent-enhanced imaging plays a critical role in clinical diagnostic imaging. Recently there has been an increasing concern regarding the chemical contrast agent used in the clinic. In this talk, Dr. Li will share his ground-breaking AI contrast-enhanced imaging, which develops state-of-the-art machine-learning techniques to eliminate the need for chemical contrast agents.

Bio:
Dr. Li is a global leader conducting multi-disciplinary research to enable artificial intelligence (AI) in clinical imaging. He is an associate professor at Case Western Reserve University. Before that, he was an associate professor in the Western University and a scientist at the Lawson Health Research Institute. He was a scientist at GE Healthcare (2006-2015). He is a committee member in multiple highly influential conferences and societies. He is most notable for serving on the prestigious board of directors in the MICCAI society (2015-2024), where he is also the general chair for the MICCAI 2022 conference, which is the most influential AI-in-imaging conference. He has over 200 publications, acted as the editor for six Springer books, and serves as an associate editor for several prestigious journals in the field. Throughout his career, he has received several awards from GE, various institutes, and international organizations.

Hosted by the Machine Intelligence for Medical Engineering Team

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