Weakly Supervised Prostate TMA Classification via Graph Convolutional Networks
This work addresses the need for objective and consistent prostate cancer grading to reduce pathologist workload and variability, though it is incremental as it builds on existing graph convolutional methods with self-supervised features.
The paper tackles the problem of subjective and variable Gleason score grading in prostate cancer by proposing a weakly-supervised graph convolutional network approach using tissue micro-array labels, achieving a 0.9659±0.0096 AUC and improvements of 39.80% and 29.27% over baseline methods.
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.