mlsanityData to 7 Oct 2026

Multiple Instance Learning for Digital Pathology: A Review on the State-of-the-Art, Limitations & Future Potential

arXiv 2206.04425 ↗First posted 9 Jun 2022cs.CVcited by 54

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Abstract (arXiv)

Digital whole slides images contain an enormous amount of information providing a strong motivation for the development of automated image analysis tools. Particularly deep neural networks show high potential with respect to various tasks in the field of digital pathology. However, a limitation is given by the fact that typical deep learning algorithms require (manual) annotations in addition to the large amounts of image data, to enable effective training. Multiple instance learning exhibits a powerful tool for learning deep neural networks in a scenario without fully annotated data. These methods are particularly effective in this domain, due to the fact that labels for a complete whole slide image are often captured routinely, whereas labels for patches, regions or pixels are not. This potential already resulted in a considerable number of publications, with the majority published in the last three years. Besides the availability of data and a high motivation from the medical perspective, the availability of powerful graphics processing units exhibits an accelerator in this field. In this paper, we provide an overview of widely and effectively used concepts of used deep multiple instance learning approaches, recent advances and also critically discuss remaining challenges and future potential.

Key facts. Cited by 54 papers in the map (data through 7 Oct 2026). Builds on 33 of its 71 references that are in the map. First posted on arXiv 9 Jun 2022.

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33 of 71 references.

References · 33

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  4. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  5. 2021Transformers in Vision: A Surveycited by 1,009
  6. 2018Self-Attention Generative Adversarial Networkscited by 809
  7. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 738
  8. 2020A Survey on Visual Transformercited by 653
  9. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  10. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  11. 2015Autoencoding beyond pixels using a learned similarity metriccited by 407
  12. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
21 more
  1. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
  2. 2016Multiple Instance Learning: A Survey of Problem Characteristics and Applicationscited by 163
  3. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  4. 2020Explainable deep learning models in medical image analysiscited by 111
  5. 2020Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primarycited by 89
  6. 2020Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Imagescited by 81
  7. 2018StainGAN: Stain Style Transfer for Digital Histological Imagescited by 68
  8. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  9. 2020Weakly supervised multiple instance learning histopathological tumor segmentationcited by 46
  10. 2022Differentiable Zooming for Multiple Instance Learning on Whole-Slide Imagescited by 43
  11. 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
  12. 2019Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Surveycited by 28
  13. 2022ScoreNet: Learning Non-Uniform Attention and Augmentation for Transformer-Based Histopathological Image Classificationcited by 21
  14. 2021A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learningcited by 19
  15. 2020Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potentialcited by 17
  16. 2021Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Imagescited by 13
  17. 2018Improving the Segmentation of Anatomical Structures in Chest Radiographs using U-Net with an ImageNet Pre-trained Encodercited by 9
  18. 2022Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutationcited by 7
  19. 2020Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networkscited by 4
  20. 2020Studying The Effect of MIL Pooling Filters on MIL Taskscited by 3
  21. 2022Evaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classificationcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 8; 2024: 14; 2025: 23; 2026: 9.

54 papers.

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  1. 2024Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysiscited by 66
  2. 2024MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Modelscited by 12
  3. 2024A Comprehensive Evaluation of Histopathology Foundation Models for Ovarian Cancer Subtype Classificationcited by 12
  4. 2025Distilled Prompt Learning for Incomplete Multimodal Survival Predictioncited by 10
  5. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  6. 2025PathDiff: Histopathology Image Synthesis with Unpaired Text and Mask Conditionscited by 8
  7. 2023Slideflow: Deep Learning for Digital Histopathology with Real-Time Whole-Slide Visualizationcited by 7
  8. 2023Dual Attention Model with Reinforcement Learning for Classification of Histology Whole-Slide Imagescited by 5
  9. 2023Towards Hierarchical Regional Transformer-based Multiple Instance Learningcited by 4
  10. 2023RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classificationcited by 4
  11. 2025Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Modelscited by 4
  12. 2023The Importance of Downstream Networks in Digital Pathology Foundation Modelscited by 3
38 more
  1. 2024Benchmarking Histopathology Foundation Models for Ovarian Cancer Bevacizumab Treatment Response Prediction from Whole Slide Imagescited by 3
  2. 2025MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classificationcited by 3
  3. 2025Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modelingcited by 3
  4. 2025PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Predictioncited by 3
  5. 2023Statistical learning on measures: an application to persistence diagramscited by 2
  6. 2023Smooth Attention for Deep Multiple Instance Learning: Application to CT Intracranial Hemorrhage Detectioncited by 2
  7. 2024Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomescited by 2
  8. 2024Biochemical Prostate Cancer Recurrence Prediction: Thinking Fast & Slowcited by 2
  9. 2025PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classificationcited by 2
  10. 2025Sequential Attention-based Sampling for Histopathological Analysiscited by 2
  11. 2025Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysiscited by 2
  12. 2025torchmil: A PyTorch-based library for deep Multiple Instance Learningcited by 2
  13. 2025Libra-MIL: Multimodal Prototypes Stereoscopic Infused with Task-specific Language Priors for Few-shot Whole Slide Image Classificationcited by 2
  14. 2026GrapHist: Graph Self-Supervised Learning for Histopathologycited by 2
  15. 2023MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classificationcited by 1
  16. 2024Integrating multiscale topology in digital pathology with pyramidal graph convolutional networkscited by 1
  17. 2024Rethinking Pre-Trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image Classificationcited by 1
  18. 2024Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohortcited by 1
  19. 2024Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Reviewcited by 1
  20. 2025Efficient Self-Supervised Grading of Prostate Cancer Pathologycited by 1
  21. 2025MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classificationcited by 1
  22. 2025MambaMIL+: Modeling Long-Term Contextual Patterns for Gigapixel Whole Slide Imagecited by 1
  23. 2026AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathologycited by 1
  24. 2024MMIL: A novel algorithm for disease associated cell type discoverycited by 0
  25. 2024MicroMIL: Graph-Based Multiple Instance Learning for Context-Aware Diagnosis with Microscopic Imagescited by 0
  26. 2024On the Generalizability of Iterative Patch Selection for Memory-Efficient High-Resolution Image Classificationcited by 0
  27. 2025Robust sensitivity control in digital pathology via tile score distribution matchingcited by 0
  28. 2025SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Imagescited by 0
  29. 2025Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Imagescited by 0
  30. 2025Fourier Transform Multiple Instance Learning for Whole Slide Image Classificationcited by 0
  31. 2025Diagnose Like A REAL Pathologist: An Uncertainty-Focused Approach for Trustworthy Multi-Resolution Multiple Instance Learningcited by 0
  32. 2025A Vector Symbolic Approach to Multiple Instance Learningcited by 0
  33. 2025Information-driven Fusion of Pathology Foundation Models for Enhanced Disease Characterizationcited by 0
  34. 2025CAPRMIL: Context-Aware Patch Representations for Multiple Instance Learningcited by 0
  35. 2025PathBench-MIL: A Comprehensive AutoML and Benchmarking Framework for Multiple Instance Learning in Histopathologycited by 0
  36. 2026Explainable histomorphology-based survival prediction of glioblastoma, IDH-wildtypecited by 0
  37. 2026Adaptive Prototype-based Interpretable Grading of Prostate Cancercited by 0
  38. 2026ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysiscited by 0

50 most cited of 54 All 54 (JSON)