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Histology-based grade classification is clinically important for many cancertypes in stratifying patients distinct treatment groups. In prostate cancer,the Gleason score is a grading system used to measure the aggressiveness ofprostate cancer from the spatial organization of cells and the distribution ofglands. However, the subjective interpretation of Gleason score often suffersfrom large interobserver and intraobserver variability. Previous work in deeplearning-based objective Gleason grading requires manual pixel-levelannotation. In this work, we propose a weakly-supervised approach for gradeclassification in tissue micro-arrays (TMA) using graph convolutional networks(GCNs), in which we model the spatial organization of cells as a graph tobetter capture the proliferation and community structure of tumor cells. Asnode-level features in our graph representation, we learn the morphometry ofeach cell using a contrastive predictive coding (CPC)-based self-supervisedapproach. We demonstrate that on a five-fold cross validation our method canachieve $0.9659\pm0.0096$ AUC using only TMA-level labels. Our methoddemonstrates a 39.80\% improvement over standard GCNs with texture features anda 29.27% improvement over GCNs with VGG19 features. Our proposed pipeline canbe used to objectively stratify low and high risk cases, reducing inter- andintra-observer variability and pathologist workload.

Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks