mlsanityData to 7 Oct 2026

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

arXiv 2004.09666 ↗First posted 20 Apr 2020eess.IV cs.CV cs.LGcited by 738

Not on the map. Why?

Cited by 27 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation and interpretability issues. These challenges have prevented the broad adaptation of computational pathology for clinical and research purposes. Here we present CLAM - Clustering-constrained attention multiple instance learning, an easy-to-use, high-throughput, and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems. CLAM is a deep-learning-based weakly-supervised method that uses attention-based learning to automatically identify sub-regions of high diagnostic value in order to accurately classify the whole slide, while also utilizing instance-level clustering over the representative regions identified to constrain and refine the feature space. In three separate analyses, we demonstrate the data efficiency and adaptability of CLAM and its superior performance over standard weakly-supervised classification. We demonstrate that CLAM models are interpretable and can be used to identify well-known and new morphological features. We further show that models trained using CLAM are adaptable to independent test cohorts, cell phone microscopy images, and biopsies. CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.

Key facts. Cited by 738 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 35; 2022: 67; 2023: 126; 2024: 169; 2025: 183; 2026: 158.

738 papers.

Sort:

By area:

  1. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  2. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  3. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  4. 2022Benchmarking Self-Supervised Learning on Diverse Pathology Datasetscited by 148
  5. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 132
  6. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
  7. 2022FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settingscited by 121
  8. 2023CellViT: Vision Transformers for Precise Cell Segmentation and Classificationcited by 108
  9. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  10. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
  11. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathologycited by 87
  12. 2023Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Predictioncited by 86
38 more
  1. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 80
  2. 2023Cross-Modal Translation and Alignment for Survival Analysiscited by 70
  3. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 70
  4. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 66
  5. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 66
  6. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  7. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 64
  8. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  9. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  10. 2024A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Modelcited by 59
  11. 2021Hierarchical Graph Representations in Digital Pathologycited by 58
  12. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  13. 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
  14. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 58
  15. 2022Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potentialcited by 54
  16. 2023Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classificationcited by 54
  17. 2023Towards a Visual-Language Foundation Model for Computational Pathologycited by 52
  18. 2024Accurate Spatial Gene Expression Prediction by integrating Multi-resolution featurescited by 52
  19. 2024Transcriptomics-guided Slide Representation Learning in Computational Pathologycited by 50
  20. 2022Self-Supervised Vision Transformers Learn Visual Concepts in Histopathologycited by 49
  21. 2023Text-guided Foundation Model Adaptation for Pathological Image Classificationcited by 47
  22. 2024Multimodal Prototyping for cancer survival predictioncited by 47
  23. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  24. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classificationcited by 42
  25. 2023A General-Purpose Self-Supervised Model for Computational Pathologycited by 42
  26. 2021A Survey on Graph-Based Deep Learning for Computational Histopathologycited by 41
  27. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  28. 2023Structured State Space Models for Multiple Instance Learning in Digital Pathologycited by 41
  29. 2024Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Predictioncited by 41
  30. 2022DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 40
  31. 2023Learned representation-guided diffusion models for large-image generationcited by 39
  32. 2024In-context learning enables multimodal large language models to classify cancer pathology imagescited by 38
  33. 2022Deep Multi-modal Fusion of Image and Non-image Data in Disease Diagnosis and Prognosis: A Reviewcited by 37
  34. 2022Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathologycited by 37
  35. 20242DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classificationcited by 35
  36. 2024WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answeringcited by 34
  37. 2022Handcrafted Histological Transformer (H2T): Unsupervised Representation of Whole Slide Imagescited by 32
  38. 2021SlideGraph+: Whole Slide Image Level Graphs to Predict HER2Status in Breast Cancercited by 31

50 most cited of 738 All 738 (JSON)