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.

本文提出了一种使用图卷积网络（GCN）进行组织微阵列（TMA）级别标注的弱监督方法，作为格里森分数的客观评级替代方案，并通过对细胞形态学特征的自监督学习，实现了对组织生成、肿瘤细胞群落结构的更好建模。五倍交叉验证结果显示，该方法可以在 TMA 级别标注下达到 0.9659±0.0096 的 AUC 值，具有显著性能提升。