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ESE Guest Seminar – “From glass to gigapixels: charting the next decade of AI in anatomic pathology”

February 17 at 11:00 AM - 12:00 PM

Pathologists have historically evaluated glass slides of excised tissue under a microscope to assess cancer and other disease. Over the last few years, the field has faced an inflection point where glass slides can be digitized, creating images greater than 10 gigapixels in size which are navigated like Google Maps. This enables pathologists to transform their clinical workflow into the digital domain. This has, in turn, begun to produce massive data sets on the order of Petabytes from which to draw biological insights and new clinical applications with the aid of machine learning. This talk begins with a brief perspective on the historical challenges of high-throughput slide digitization and explores the promise of AI in clinical practice. I will then focus on the challenges that we face using image-based AI in high-stakes medical applications, which include generalizability, data drift, and implementation, and suggest engineering and computing approaches to solve them.

Mark Zarella

Associate Professor and Vice Chair of Digital and Computational Pathology, Penn Medicine

Mark Zarella, PhD is Associate Professor and Vice Chair of Digital and Computational Pathology at Penn Medicine. Prior to joining Penn Medicine, he served as the Scientific Director of the Division of Computational Pathology & AI at the Mayo Clinic. Before joining Mayo, he was the Director of Digital Pathology at Johns Hopkins Medicine, holding a secondary appointment in Biomedical Engineering. Dr. Zarella has served on the Digital and Computational Pathology Committee for the College of American Pathologists, the Board of Directors for the Digital Pathology Association, and numerous institutional boards related to clinical informatics, AI governance, and high-performance computing. His research focus is on establishing and refining best practices to ensure the responsible deployment of AI tools in pathology, which includes establishing novel techniques for quantitative evaluation of AI models to estimate risk.

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