用于组织病理学全切片图像分析的基于切片的图协同训练
Slide-based Graph Collaborative Training for Histopathology Whole Slide Image Analysis
AI总结：
针对现有WSI分析忽略切片间关联的问题，提出通用流水线SlideGCD，通过图协同训练将癌症发展先验知识融入端到端工作流以优化切片表示，在4个任务和7个SOTA框架上验证了有效性和鲁棒性。
摘要：
计算病理学的发展基于这样一个共识：肿瘤的病理特征是癌症诊断的重要指导。大多数现有研究集中在每张WSI内的内部上下文信息上，却忽略了切片之间可能的相互关联。由于肿瘤的发展是一个涉及一系列随时间累积的组织学、形态学和遗传学变化的连续过程，不同阶段、分级、位置和患者之间的WSI的相似性和差异应该有助于WSI的表示，并值得在WSI建模中被考虑。为了验证将切片间相互关联引入WSI表示学习的先进性，我们提出了一种通用的WSI分析流水线SlideGCD，它可以适应任何现有的多示例学习（MIL）框架并提升其性能。在这种新范式下，癌症发展的先验知识可以参与端到端工作流，同时初始化和细化切片表示，作为基于切片的图中消息传递的指导。我们进行了广泛的比较和实验，以验证所提出的流水线在4个不同任务（包括癌症亚型分类、癌症分期、生存预测和基因突变预测）中的有效性和鲁棒性，使用了7个具有代表性的SOTA WSI分析框架作为骨干。
英文摘要：
The development of computational pathology lies in the consensus that pathological characteristics of tumors are significant guidance for cancer diagnostics. Most existing research focuses on the inner-contextual information within each WSI yet ignores the possible inter-correlations between slides. As the development of tumors is a continuous process involving a series of histological, morphological, and genetic changes that accumulate over time, the similarities and differences between WSIs across various stages, grades, locations and patients should potentially contribute to the representation of WSIs and deserve to be taken into account in WSI modeling. To verify the advancement of introducing the slide inter-correlations into the representation learning of WSIs, we proposed a generic WSI analysis pipeline SlideGCD that can be adapted to any existing Multiple Instance Learning (MIL) frameworks and improve their performance. With the new paradigm, the prior knowledge of cancer development can participate in the end-to-end workflow, which concurrently initializes and refines the slide representation, as a guide for message passing in the slide-based graph. Extensive comparisons and experiments are conducted to validate the effectiveness and robustness of the proposed pipeline across 4 different tasks, including cancer subtyping, cancer staging, survival prediction, and gene mutation prediction, with 7 representative SOTA WSI analysis frameworks as backbones.
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。
