Existing WSI analysis methods lie on the consensus that histopathological characteristics of tumors are significant guidance for cancer diagnostics. Particularly, as the evolution of cancers is a continuous process, the correlations and differences across various stages, anatomical locations and patients should be taken into account. However, recent research mainly focuses on the inner-contextual information in a single WSI, ignoring the correlations between slides. To verify whether introducing the slide inter-correlations can bring improvements to WSI representation learning, we propose a generic WSI analysis pipeline SlideGCD that considers the existing multi-instance learning (MIL) methods as the backbone and forge the WSI classification task as a node classification problem. More specifically, SlideGCD declares a node buffer that stores previous slide embeddings for subsequent extensive slide-based graph construction and conducts graph learning to explore the inter-correlations implied in the slide-based graph. Moreover, we frame the MIL classifier and graph learning into two parallel workflows and deploy the knowledge distillation to transfer the differentiable information to the graph neural network. The consistent performance boosting, brought by SlideGCD, of four previous state-of-the-art MIL methods is observed on two TCGA benchmark datasets. The code is available at https://github.com/HFUT-miaLab/SlideGCD.

通过考虑幻灯片之间的互相关联性，我们提出了一个通用的WSI分析流水线SlideGCD，将现有的多实例学习方法作为骨干，并将WSI分类任务构建为节点分类问题。SlideGCD通过存储先前幻灯片嵌入来进行广泛基于幻灯片的图构建，并进行图学习来探索幻灯片之间的互相关联性，进而实现MIL分类器和图学习的两个并行工作流，并通过知识蒸馏将可区分信息传递给图神经网络。在两个TCGA基准数据集上观察到SlideGCD带来的一致性性能提升，超过了四种最先进的MIL方法。

SlideGCD：用知识蒸馏的滑动图协作训练方法进行整张切片图像分类