通过拼图方式恢复组织切片实例顺序，提升病理图像分析效果
Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysis
- 将切片实例打乱后重建顺序，捕捉空间语义关联
- 在分类与生存预测任务上超越当前最优MIL方法
- 适合需要精准建模组织结构关系的病理分析研究者
尽管多实例学习（MIL）在组织病理学全切片图像（WSI）分析中表现良好，但其依赖排列不变性，严重限制了对实例间语义关联的挖掘能力。基于实证与理论分析，我们提出一种非排列不变的新范式：通过学习从随机打乱的实例中恢复原始顺序来揭示语义关联。该任务称为“实例拼图破解”，我们设计了一种基于最优传输理论的新型孪生网络解决方案。在全切片图像分类与生存预测任务上的实验表明，该方法优于近期最先进的MIL模型。代码已开源：https://github.com/xiwenc1/MIL-JigsawPuzzles。
原文摘要 · Abstract (English)
While multiple instance learning (MIL) has shown to be a promising approach for histopathological whole slide image (WSI) analysis, its reliance on permutation invariance significantly limits its capacity to effectively uncover semantic correlations between instances within WSIs. Based on our empirical and theoretical investigations, we argue that approaches that are not permutation-invariant but better capture spatial correlations between instances can offer more effective solutions. In light of these findings, we propose a novel alternative to existing MIL for WSI analysis by learning to restore the order of instances from their randomly shuffled arrangement. We term this task as cracking an instance jigsaw puzzle problem, where semantic correlations between instances are uncovered. To tackle the instance jigsaw puzzles, we propose a novel Siamese network solution, which is theoretically justified by optimal transport theory. We validate the proposed method on WSI classification and survival prediction tasks, where the proposed method outperforms the recent state-of-the-art MIL competitors. The code is available at https://github.com/xiwenc1/MIL-JigsawPuzzles.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。