AIMI-IBIIS Seminar: Going Beyond What is Humanly Possible: Machine Learning for Clinical Pathology - Geert Litjens, PhD

via Zoom - email aimicenter@stanford.edu for link

Open to Stanford community only

Abstract:
Machine learning advances in the past decade have revolutionized computer vision, and have since also made their way into medical imaging. Although many associate medical imaging with radiology first, clinical pathology might be more significantly impacted, at least in the short term by machine learning. Partly due to the simultaneous advent of machine learning in medicine and digital pathology, but perhaps mostly because in pathology achieving inhuman feats is surprisingly low-hanging fruit. In this presentation, I will give some examples analog to radiology, then illustrate some superhuman applications. Subsequently, I will highlight our current limitations and possible future research directions.

About:
Geert Litjens is an assistant professor of Computational Pathology at the Radboud University Medical Center. His research is at the intersection of machine learning, medical imaging, and oncology. He co-chairs the Computational Pathology Group, which develops automated machine learning systems for cancer detection, biomarker discovery and quantification, and improved prognostication. 

He is also the developer of the ASAP software package for analyzing and visualizing whole-slide images and (co-)organizer of several high-profile challenges in medical imaging such as PROMISE12, CAMELYON, and PANDA. He (co-)authored over 60 publications in medical, imaging, and machine learning conferences and journals.