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.

翻译：多示例学习（MIL）已广泛应用于全切片病理图像的表征。然而，实例间的空间、语义及决策纠缠限制了其表征能力与可解释性。为解决这些挑战，我们提出了一种隐因子分组增强的聚类推理实例解缠学习框架，用于全切片图像（WSI）的可解释表征，该框架包含三个阶段。首先，我们引入一种新颖的正半定隐因子分组方法，将实例映射到隐子空间中，有效缓解MIL中的空间纠缠。为减轻语义纠缠，我们通过聚类推理实例解缠，采用实例概率反事实推断与优化方法。最后，我们通过实例效应重加权实现广义线性加权决策，以解决决策纠缠问题。在多中心数据集上的大量实验表明，我们的模型性能优于所有现有先进模型。此外，通过解缠表征和透明的决策过程，该模型实现了与病理学家认知一致的可解释性。