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

TL;DR

This paper introduces a novel framework for whole-slide image representation that disentangles spatial, semantic, and decision entanglements, improving interpretability and performance in pathology image analysis.

Contribution

The paper proposes a positive semi-definite latent factor grouping and cluster-reasoning approach for disentangled learning in WSI, addressing multiple entanglements simultaneously.

Findings

Outperforms state-of-the-art models on multicentre datasets.

Achieves pathologist-aligned interpretability.

Enhances representation quality and decision transparency.

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.