In digital pathology, the multiple instance learning (MIL) strategy is widely
used in the weakly supervised histopathology whole slide image (WSI)
classification task where giga-pixel WSIs are only labeled at the slide level.
However, existing attention-based MIL approaches often overlook contextual
information and intrinsic spatial relationships between neighboring tissue
tiles, while graph-based MIL frameworks have limited power to recognize the
long-range dependencies. In this paper, we introduce the integrative
graph-transformer framework that simultaneously captures the context-aware
relational features and global WSI representations through a novel Graph
Transformer Integration (GTI) block. Specifically, each GTI block consists of a
Graph Convolutional Network (GCN) layer modeling neighboring relations at the
local instance level and an efficient global attention model capturing
comprehensive global information from extensive feature embeddings. Extensive
experiments on three publicly available WSI datasets: TCGA-NSCLC, TCGA-RCC and
BRIGHT, demonstrate the superiority of our approach over current
state-of-the-art MIL methods, achieving an improvement of 1.0% to 2.6% in
accuracy and 0.7%-1.6% in AUROC.

通过引入整合图转换器框架，本研究在数字病理学中用于弱监督组织病理学全幻灯片图像分类的多实例学习策略可以同时捕捉上下文感知的关系特征和全局图像表示，实验证明该方法在 WSI 数据集上具有优越性能，准确率提高 1.0%-2.6%，在 AUROC 上提高 0.7%-1.6%。