正半定潜在因子分组增强的聚类推理实例解耦学习用于WSI表示
Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation
AI总结：
针对WSI中实例空间、语义和决策纠缠问题，提出正半定潜在因子分组增强的聚类推理实例解耦学习框架，通过三阶段方法有效提升表示能力与可解释性，在多中心数据集上超越现有模型。
摘要：
多示例学习（MIL）已被广泛用于表示全切片病理图像。然而，实例间的空间、语义和决策纠缠限制了其表示能力和可解释性。为了应对这些挑战，我们提出了一种潜在因子分组增强的聚类推理实例解耦学习框架，分三个阶段进行全切片图像（WSI）的可解释表示。首先，我们引入一种新颖的正半定潜在因子分组，将实例映射到潜在子空间，有效缓解MIL中的空间纠缠。为了缓解语义纠缠，我们通过聚类推理实例解耦采用实例概率反事实推理和优化。最后，我们通过实例效应重新加权采用广义线性加权决策来解决决策纠缠。在多中心数据集上的大量实验表明，我们的模型优于所有最先进的模型。此外，它通过解耦表示和透明的决策过程实现了与病理学家对齐的可解释性。
英文摘要：
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.
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