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

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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.
Chakraborty, Souradeep, Rajarsi Gupta, Ke Ma, Darshana Govind, Pinaki Sarder, Won-Tak Choi, Waqas Mahmud, et al. 2022. “Predicting the Visual Attention of Pathologists Evaluating Whole Slide Images of Cancer.” In Medical Optical Imaging and Virtual Microscopy Image Analysis, 11–21. Springer Nature Switzerland.