Deep Learning in Radiology – Emerging Trends and New Research



Despite the growing popularity of deep learning neural networks for various medical imaging applications, the vast majority of algorithms to date represent early proof-of-concept designs that will require a degree of evolution before achieving practical clinical utility. In this talk, we will explore several of these key shortcomings and also discuss new emerging research trends that aim to bridge this gap, including topics such as unsupervised and semi-supervised learning, federated learning and clinical implementation challenges.

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Peter D. Chang is an Assistant Professor-in-Residence for the Departments of Radiological Sciences and Computer Science at UC Irvine and Director for the Center for Artificial Intelligence in Diagnostic Medicine (CAIDM), a multi-specialty initiative to develop and integrate artificial intelligence technology across the UC Irvine healthcare system. He is also a co-founder of multiple AI startups including most recently, a company focused on deep learning for medical imaging diagnosis. Dr. Chang’s unique perspective arises from experience both as a radiologist physician and full-stack software engineer with over a decade of experience building FDA-cleared tools used in hospitals around the world.

MGH & BWH Center for Clinical Data Science

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Nov 11 2021


12:00 pm - 1:00 pm

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