Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks

TL;DR

This paper introduces a weakly supervised graph convolutional network approach for prostate cancer grading from tissue micro-arrays, achieving high accuracy with minimal manual annotation.

Contribution

It proposes a novel GCN-based method that models cell organization and learns cell morphometry using self-supervised learning, reducing the need for detailed annotations.

Findings

Achieves 0.9659 AUC with only TMA-level labels

Outperforms standard GCNs by nearly 40%

Reduces inter- and intra-observer variability in grading

Abstract

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