mlsanityData to 7 Oct 2026

Improving Representation Learning for Histopathologic Images with Cluster Constraints

arXiv 2310.12334 ↗First posted 18 Oct 2023cs.CVcited by 8

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Abstract (arXiv)

Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides - a process that is both labor-intensive and time-consuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 30 of its 46 references that are in the map. First posted on arXiv 18 Oct 2023.

Builds on

30 of 46 references.

References · 30

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18 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 4.

8 papers.

  1. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  2. 2024Learning Spatial-Preserving Hierarchical Representations for Digital Pathologycited by 1
  3. 2024Efficient Self-Supervised Barlow Twins from Limited Tissue Slide Cohorts for Colonic Pathology Diagnosticscited by 1
  4. 2026Exploiting Label-Independent Regularization from Spatial Dependencies for Whole Slide Image Analysiscited by 1
  5. 2026HiST: A Hierarchical Sparse Transformer for Cross-Modal Spatial Transcriptomics Modelingcited by 1
  6. 2025Leveraging Spatial Context for Positive Pair Sampling in Histopathology Image Representation Learningcited by 0
  7. 2026Prototype Instance-semantic Disentanglement with Low-rank Regularized Subspace Clustering for WSIs Explainable Recognitioncited by 0
  8. 2026MacNet: An End-to-End Manifold-Constrained Adaptive Clustering Network for Interpretable Whole Slide Image Classificationcited by 0