MedAI Session 26: Towards Generalist Imaging Using Multimodal Self-supervised Learning | Mars Huang

Title: Towards Generalist Medical Imaging AI Using Multimodal Self-supervised Learning

Speaker: Mars Huang

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.

Speaker Bio:
Mars Huang is a 3rd year Ph.D. student in Biomedical Informatics at Stanford University, co-advised by Matthew P. Lungren and Serena Yeung. 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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