The Taub Faculty of Computer Science Events and Talks

Digital analysis of pathology whole-slide images is fast becoming a game changer in cancer diagnosis and treatment. Specifically, deep learning methods have shown great potential to support pathology analysis, with recent studies identifying molecular traits that were not previously recognized in pathology H&E whole-slide images. Simultaneous to these developments, it is becoming increasingly evident that tumor heterogeneity is an important determinant of cancer prognosis and susceptibility to treatment, and should therefore play a role in the evolving practices of matching treatment protocols to patients. In this talk, I will present our work on spatially resolving bulk mRNA and miRNA expression levels on pathology whole-slide images (WSIs). I will further present a statistical method we developed to spatially characterize tumor heterogeneity from the inferred gene expression levels and demonstrate that it applies to a wide variety of spatial data, including 3D data like brain MRI scans. Finally, I will present a deep learning model we developed to predict DNA methylation levels.