Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation

• URL: http://arxiv.org/abs/2511.01304v2
• Date: Tue, 04 Nov 2025 16:36:28 GMT
• Title: Positive Semi-definite Latent Factor Grouping-Boosted Cluster-reasoning Instance Disentangled Learning for WSI Representation
• Abstract summary: We propose a latent factor grouping-boosted cluster-reasoning instance disentangled learning framework for whole-slide image (WSI) interpretable representation.<n>First, we introduce a novel positive semi-definite latent factor grouping that maps instances into a latent subspace.<n>To alleviate semantic entanglement, we employ instance probability counterfactual inference and optimization.<n>Finally, we employ a generalized linear weighted decision via instance effect re-weighting to address decision entanglement.
• Score: 17.801036827103562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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