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AIMI Research Meeting: Towards Generalist Medical Imaging AI Using Multimodal Self-supervised Learning - Mars Huang, PhD Student

Event Details:

Thursday, May 19, 2022
3:00pm - 4:00pm PDT

This event is open to:

Alumni/Friends
Faculty/Staff
General Public
Members
Students

 

Abstract
In recent years, deep learning models have demonstrated superior diagnostic accuracy compared to human physicians in several medical domains and imaging modalities. While deep learning and computer vision provide promising solutions for automating medical image analysis, annotating medical imaging datasets requires domain expertise and is cost-prohibitive at scale. Therefore, the task of building effective medical imaging models is often hindered by the lack of large-scale manually labeled datasets. In a healthcare system where myriad opportunities and possibilities for automation exist, it is practically impossible to curate labeled datasets for all tasks, modalities, and outcomes for training supervised models. Therefore, it is important to develop strategies for training generalist medical AI models without the need for large-scale labeled datasets. In this talk, I will talk about how our group plan to develop generalist medical imaging models by combining multimodal fusion techniques with self-supervised learning.


About
Mars Huang is a 4th-year Ph.D. student in Biomedical Informatics at Stanford University, co-advised by Serena Yeung and Nigam Shah. He is interested in combining self-supervised learning and multimodal fusion techniques for medical imaging applications. Previously, he completed his undergraduate studies at the University of California, San Diego, majoring in Computer Science and Bioinformatics. 

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