提出新框架解耦病理切片中实例的时空语义纠缠，提升可解释性。
Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation
- 通过正半定隐因子分组缓解空间混淆
- 在多中心数据上超越现有最优模型
- 生成医生可理解的解耦表示与透明决策
多重实例学习（MIL）广泛用于全切片病理图像（WSI）表征，但实例间的空间、语义和决策纠缠限制了其表达能力与可解释性。为此，我们提出一种三阶段的隐因子分组增强聚类推理实例解耦学习框架，实现可解释的WSI表征。首先引入正半定隐因子分组，将实例映射至隐子空间，有效缓解MIL中的空间纠缠；为减轻语义纠缠，采用实例概率反事实推断与基于聚类推理的实例解耦优化；最后通过广义线性加权决策与实例效应重加权解决决策纠缠。在多个中心数据集上的大量实验表明，该模型优于所有现有先进方法，并通过解耦表示与透明决策过程实现病理科医生对齐的可解释性。
原文摘要 · Abstract (English)
Multiple instance learning (MIL) has been widely used for representing whole-slide pathology images. However, spatial, semantic, and decision entanglements among instances limit its representation and interpretability. To address these challenges, we propose a latent factor grouping-boosted cluster-reasoning instance disentangled learning framework for whole-slide image (WSI) interpretable representation in three phases. First, we introduce a novel positive semi-definite latent factor grouping that maps instances into a latent subspace, effectively mitigating spatial entanglement in MIL. To alleviate semantic entanglement, we employs instance probability counterfactual inference and optimization via cluster-reasoning instance disentangling. Finally, we employ a generalized linear weighted decision via instance effect re-weighting to address decision entanglement. Extensive experiments on multicentre datasets demonstrate that our model outperforms all state-of-the-art models. Moreover, it attains pathologist-aligned interpretability through disentangled representations and a transparent decision-making process.
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。