mlsanityData to 14 Oct 2026

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

arXiv 2511.01304 ↗First posted 3 Nov 2025cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 3 Nov 2025.

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References · 22

  1. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  2. 2017Deep Setscited by 1,421
  3. 2018Attention-based Deep Multiple Instance Learningcited by 1,095
  4. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  5. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  6. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  7. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  8. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 256
  9. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 211
  10. 2020Unsupervised Domain Adaptation via Structurally Regularized Deep Clusteringcited by 140
  11. 2022Disentangled Representation Learningcited by 112
  12. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
10 more
  1. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 72
  2. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 67
  3. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  4. 2019Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networkscited by 43
  5. 2023Structured State Space Models for Multiple Instance Learning in Digital Pathologycited by 41
  6. 2023Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignmentcited by 33
  7. 2023GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clusteringcited by 30
  8. 2024DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classificationcited by 25
  9. 2024cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Processcited by 5
  10. 2024Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learningcited by 1

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