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。

基于图卷积网络的前列腺 TMA 弱监督分类

Weakly Supervised Prostate TMA Classification via Graph Convolutional  Networks

Automated grading of prostate cancer histopathology images is a challenging
task, with one key challenge being the scarcity of annotations down to the
level of regions of interest (strong labels), as typically the prostate cancer
Gleason score is known only for entire tissue slides (weak labels). In this
study, we focus on automated Gleason score assignment of prostate cancer
whole-slide images on the basis of a large weakly-labeled dataset and a smaller
strongly-labeled one. We efficiently leverage information from both label
sources by jointly training a classifier on the two datasets and by introducing
a gradient update scheme that assigns different relative importances to each
training example, as a means of self-controlling the weak supervision signal.
Our approach achieves superior performance when compared with standard Gleason
scoring methods.

本研究通过联合训练两个数据集以及引入一种梯度更新机制，旨在自控弱监督信号，基于大型弱标记数据集和小型强标记数据集，自动赋予前列腺癌全层切片的格里森分数的任务。我们的方法在与标准格里森评分方法比较时取得了优异的性能。