mlsanityData to 7 Oct 2026

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

arXiv 2106.00908 ↗First posted 2 Jun 2021cs.CVcited by 549

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Cited by 20 in State-space sequence models (S4, Mamba), but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL methods are usually based on independent and identical distribution hypothesis, thus neglect the correlation among different instances. To address this problem, we proposed a new framework, called correlated MIL, and provided a proof for convergence. Based on this framework, we devised a Transformer based MIL (TransMIL), which explored both morphological and spatial information. The proposed TransMIL can effectively deal with unbalanced/balanced and binary/multiple classification with great visualization and interpretability. We conducted various experiments for three different computational pathology problems and achieved better performance and faster convergence compared with state-of-the-art methods. The test AUC for the binary tumor classification can be up to 93.09% over CAMELYON16 dataset. And the AUC over the cancer subtypes classification can be up to 96.03% and 98.82% over TCGA-NSCLC dataset and TCGA-RCC dataset, respectively. Implementation is available at: https://github.com/szc19990412/TransMIL.

Key facts. Cited by 549 papers in the map (data through 7 Oct 2026). Builds on 32 of its 41 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

32 of 41 references.

References · 32

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  4. 2020End-to-End Object Detection with Transformerscited by 7,384
  5. 2017Squeeze-and-Excitation Networkscited by 4,763
  6. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  7. 2020Deformable DETR: Deformable Transformers for End-to-End Object Detectioncited by 3,410
  8. 2018CBAM: Convolutional Block Attention Modulecited by 2,463
  9. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
  10. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  11. 2020Pre-Trained Image Processing Transformercited by 891
  12. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
20 more
  1. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  2. 2021Nystr"omformer: A Nystr"om-Based Algorithm for Approximating Self-Attentioncited by 468
  3. 2021Conditional Positional Encodings for Vision Transformerscited by 402
  4. 2020Learning Texture Transformer Network for Image Super-Resolutioncited by 346
  5. 2019Lookahead Optimizer: k steps forward, 1 step backcited by 333
  6. 2021TransFuse: Fusing Transformers and CNNs for Medical Image Segmentationcited by 258
  7. 2020How Much Position Information Do Convolutional Neural Networks Encode?cited by 171
  8. 2019Deep neural network models for computational histopathology: A surveycited by 165
  9. 2020Learning Joint Spatial-Temporal Transformations for Video Inpaintingcited by 162
  10. 2016Revisiting Multiple Instance Neural Networkscited by 119
  11. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  12. 2019CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentationcited by 73
  13. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  14. 2020Weakly supervised multiple instance learning histopathological tumor segmentationcited by 46
  15. 2021A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approachescited by 44
  16. 2018Attention-Based Deep Neural Networks for Detection of Cancerous and Precancerous Esophagus Tissue on Histopathological Slidescited by 29
  17. 2019Multiple instance learning with graph neural networkscited by 25
  18. 2020A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networkscited by 25
  19. 2019Patch Transformer for Multi-tagging Whole Slide Histopathology Imagescited by 13
  20. 2020Renal Cell Carcinoma Detection and Subtyping with Minimal Point-Based Annotation in Whole-Slide Imagescited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 52; 2023: 97; 2024: 131; 2025: 142; 2026: 126.

549 papers.

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  1. 2022Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learningcited by 357
  2. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  3. 2022Transformers in Medical Imaging: A Surveycited by 245
  4. 2023Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Imagescited by 132
  5. 2023Artificial Intelligence for Digital and Computational Pathologycited by 106
  6. 2024HEST-1k: A Dataset for Spatial Transcriptomics and Histology Image Analysiscited by 104
  7. 2023Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Predictioncited by 92
  8. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  9. 2024MambaMIL: Enhancing Long Sequence Modeling with Sequence Reordering in Computational Pathologycited by 87
  10. 2023Multimodal Optimal Transport-based Co-Attention Transformer with Global Structure Consistency for Survival Predictioncited by 86
  11. 2022A graph-transformer for whole slide image classificationcited by 84
  12. 2024PRISM: A Multi-Modal Generative Foundation Model for Slide-Level Histopathologycited by 80
38 more
  1. 2023Cross-Modal Translation and Alignment for Survival Analysiscited by 70
  2. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 70
  3. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 66
  4. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 66
  5. 2024A Clinical Benchmark of Public Self-Supervised Pathology Foundation Modelscited by 64
  6. 2023Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Reviewcited by 62
  7. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  8. 2024A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Modelcited by 59
  9. 2022Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathologycited by 58
  10. 2023Multiple Instance Learning Framework with Masked Hard Instance Mining for Whole Slide Image Classificationcited by 58
  11. 2022Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potentialcited by 54
  12. 2023Task-specific Fine-tuning via Variational Information Bottleneck for Weakly-supervised Pathology Whole Slide Image Classificationcited by 54
  13. 2024Accurate Spatial Gene Expression Prediction by integrating Multi-resolution featurescited by 52
  14. 2024Transcriptomics-guided Slide Representation Learning in Computational Pathologycited by 50
  15. 2024Multimodal Prototyping for cancer survival predictioncited by 47
  16. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  17. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classificationcited by 42
  18. 2022Transformers in Medical Image Analysis: A Reviewcited by 41
  19. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  20. 2023Structured State Space Models for Multiple Instance Learning in Digital Pathologycited by 41
  21. 2024Prototypical Information Bottlenecking and Disentangling for Multimodal Cancer Survival Predictioncited by 41
  22. 2024Visual Mamba: A Survey and New Outlookscited by 41
  23. 2022DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 40
  24. 2024MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generationcited by 38
  25. 2025PathFinder: A Multi-Modal Multi-Agent System for Medical Diagnostic Decision-Making Applied to Histopathologycited by 36
  26. 20242DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classificationcited by 35
  27. 2025Distilling foundation models for robust and efficient models in digital pathologycited by 33
  28. 2023Pathology-and-genomics Multimodal Transformer for Survival Outcome Predictioncited by 32
  29. 2025Medical World Model: Generative Simulation of Tumor Evolution for Treatment Planningcited by 31
  30. 2022Towards Label-efficient Automatic Diagnosis and Analysis: A Comprehensive Survey of Advanced Deep Learning-based Weakly-supervised, Semi-supervised and Self-supervised Techniques in Histopathological Image Analysiscited by 29
  31. 2025ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classificationcited by 28
  32. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
  33. 2024Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interactioncited by 27
  34. 2024Multistain Pretraining for Slide Representation Learning in Pathologycited by 27
  35. 2022Transformer based multiple instance learning for weakly supervised histopathology image segmentationcited by 26
  36. 2023ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysiscited by 25
  37. 2023SI-MIL: Taming Deep MIL for Self-Interpretability in Gigapixel Histopathologycited by 25
  38. 2022Giga-SSL: Self-Supervised Learning for Gigapixel Imagescited by 24

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