arXiv 2407.089682024-07-19cs.CV

SlideGCD：基于切片的图协同训练与知识蒸馏用于全切片图像分类

SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification

School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机与信息工程学院）；School of Software, Hefei University of Technology（合肥工业大学软件学院）；Image Processing Center, School of Astronautics, Beihang University（北京航空航天大学宇航学院图像处理中心）；Beijing Advanced Innovation Center on Biomedical Engineering（北京生物医学工程高精尖创新中心）

Tong Shu, Jun Shi, Dongdong Sun, Zhiguo Jiang, Yushan Zheng

备注：: Accepted for MICCAI 2024

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AI总结：

提出SlideGCD，将WSI分类视为节点分类，通过构建切片间图并利用知识蒸馏协同训练，提升多种MIL方法的性能。

摘要：

现有的全切片图像（WSI）分析方法基于一个共识，即肿瘤的组织病理学特征对癌症诊断具有重要指导意义。特别是，由于癌症的演变是一个连续的过程，应考虑不同阶段、解剖位置和患者之间的相关性和差异。然而，近期研究主要关注单个WSI内部的上下文信息，忽略了切片之间的相关性。为了验证引入切片间相关性是否能改善WSI表示学习，我们提出了一种通用的WSI分析流程SlideGCD，该流程将现有的多实例学习（MIL）方法作为骨干，并将WSI分类任务构建为节点分类问题。具体而言，SlideGCD声明了一个节点缓冲区，用于存储先前的切片嵌入，以便后续构建广泛的基于切片的图，并进行图学习以探索基于切片的图中隐含的互相关性。此外，我们将MIL分类器和图学习构建为两个并行的工作流，并部署知识蒸馏将可区分的信息传递给图神经网络。在两个TCGA基准数据集上，观察到SlideGCD对四种先前最先进的MIL方法带来了一致的性能提升。代码可在https://github.com/HFUT-miaLab/SlideGCD获取。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。