Abstract
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 this https URL.
Abstract (translated)
虽然多实例学习（MIL）已被证明是用于组织病理学全切片图像（WSI）分析的一种有前途的方法，但其依赖于置换不变性显著限制了它有效揭示WSI内实例间语义关联的能力。基于我们的实证和理论研究，我们认为非置换不变性的方法能够更好地捕捉实例间的空间相关性，并能提供更有效的解决方案。鉴于这些发现，我们提出了一种针对现有MIL的新替代方案，该方案通过学习从随机混洗的排列中恢复实例顺序来进行WSI分析。我们将这一任务称为破解实例拼图问题，在此过程中揭示了实例之间的语义关联。为了应对实例拼图挑战，我们提出了一个新颖的暹罗网络解决方案，其理论依据来自于最优传输理论。我们在WSI分类和生存预测任务上验证了所提出的方法，结果显示该方法优于最近的状态-of-the-art MIL竞争对手。代码可在提供的链接处获取。
URL
https://arxiv.org/abs/2507.08178
