Expert-Informed, User-Centric Explanations of Medical Image Classification
Guest Speaker: Dr. Michael Pazzani is the Director of the AI4Health Center and Research Director, Distinguished Principal Scientist, and Senior Supervising Computer Scientist of the Information Sciences Institute at the University of Southern California
Moderated by Dr. Reza Rawassizadeh, Associate Prof of CS
Friday, October 4, 2024 at 4:00 PM EST
Abstract: We argue that the dominant approach to explainable AI for explaining image classification with deep learning– annotating images with heatmaps, provides little value for users unfamiliar with deep learning. Instead, we argue that explainable AI for images should produce output like experts produce when communicating with one another, with apprentices, and with novices. We discuss a bit of the history of interpretable and explainable AI with examples from AI & medicine. A new approach that labels image regions with diagnostic features is proposed and evaluated. We draw on examples from radiology, ophthalmology, dermatology as well as bird classification.
Bio: Michael Pazzani is the director of the AI4Health Center at the University of Southern California. Dr Pazzani received his Ph.D. Computer Science, University of California, Los Angeles, and is a fellow of the Association for the Advancement of Artificial Intelligence. Dr. Pazzani started his career as an assistant, associate, and full professor of Information and Computer Science at the University of California, Irvine, and has served as the Director of the Information and Intelligent Systems Division at the National Science Foundation and as a member of the Board of Regents of the National Library of Medicine at the National Institutes of Health