Visual attention analysis of pathologists examining whole slide images of Prostate cancer
Souradeep Chakraborty, Ke Ma, Rajarsi Gupta, Beatrice Knudsen, Gregory, J. Zelinsky, Joel H. Saltz, Dimitris Samaras

TL;DR
This study analyzes how pathologists examine prostate cancer whole slide images, revealing attention patterns and developing a deep learning model that predicts visual attention correlating with tumor regions.
Contribution
First detailed analysis of pathologists' navigation in prostate cancer WSIs and a deep learning model predicting attention maps aligned with tumor locations.
Findings
Attention heatmaps correlate with tumor annotations
Deep learning model predicts attention with high accuracy
Pathologists' navigation patterns differ between specialists and generalists
Abstract
We study the attention of pathologists as they examine whole-slide images (WSIs) of prostate cancer tissue using a digital microscope. To the best of our knowledge, our study is the first to report in detail how pathologists navigate WSIs of prostate cancer as they accumulate information for their diagnoses. We collected slide navigation data (i.e., viewport location, magnification level, and time) from 13 pathologists in 2 groups (5 genitourinary (GU) specialists and 8 general pathologists) and generated visual attention heatmaps and scanpaths. Each pathologist examined five WSIs from the TCGA PRAD dataset, which were selected by a GU pathology specialist. We examined and analyzed the distributions of visual attention for each group of pathologists after each WSI was examined. To quantify the relationship between a pathologist's attention and evidence for cancer in the WSI, we obtained…
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