Predicting the Visual Attention of Pathologists Evaluating Whole Slide Images of Cancer

Abstract

This work presents PathAttFormer, a deep learning model that predicts the visual attention of pathologists viewing whole slide images (WSIs) while evaluating cancer. This model has two main components: (1) a patch-wise attention prediction module using a Swin transformer backbone and (2) a self-attention based attention refinement module to compute pairwise-similarity between patches to predict spatially consistent attention heatmaps. We observed a high level of agreement between model predictions and actual viewing behavior, collected by capturing panning and zooming movements using a digital microscope interface. Visual attention was analyzed in the evaluation of prostate cancer and gastrointestinal neuroendocrine tumors (GI-NETs), which differ greatly in terms of diagnostic paradigms and the demands on attention. Prostate cancer involves examining WSIs stained with Hematoxylin and Eosin (H &E) to identify distinct growth patterns for Gleason grading. In contrast, GI-NETs require a multi-step approach of identifying tumor regions in H &E WSIs and grading by quantifying the number of Ki-67 positive tumor cells highlighted with immunohistochemistry (IHC) in a separate image. We collected attention data from pathologists viewing prostate cancer H &E WSIs from The Cancer Genome Atlas (TCGA) and 21 H &E WSIs of GI-NETs with corresponding Ki-67 IHC WSIs. This is the first work that utilizes the Swin transformer architecture to predict visual attention in histopathology images of GI-NETs, which is generalizable to predicting attention in the evaluation of multiple sequential images in real world diagnostic pathology and IHC applications.