Towards Bayesian Uncertainty Quantification in Deep Learning Models for Brain Tumor Segmentation

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Presenters: Xun Huan, Assistant Professor, Mechanical Engineering

While the use of deep learning models in healthcare has grown rapidly in recent years, the uncertainty and confidence in their predictions is often unavailable and unreported. A lack of such information can render decision-making dangerous, and prompt clinicians to hesitate in using and trusting these machine learning technologies. Listen to Prof. Huan (Mechanical Engineering) explains how his team is tackling this issue using principles and computational methods of uncertainty quantification for artificial intelligence applications in medicine, focusing on the problem of brain tumor segmentation from MRI scans.

Created by Michigan Institute for Computational Discovery for @UM_MICDE within the Office of Research at the University of Michigan.

University of Michigan

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