mlsanityData to 14 Oct 2026

DGMIL: Distribution Guided Multiple Instance Learning for Whole Slide Image Classification

arXiv 2206.08861 ↗First posted 17 Jun 2022cs.CVcited by 40

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Abstract (arXiv)

Multiple Instance Learning (MIL) is widely used in analyzing histopathological Whole Slide Images (WSIs). However, existing MIL methods do not explicitly model the data distribution, and instead they only learn a bag-level or instance-level decision boundary discriminatively by training a classifier. In this paper, we propose DGMIL: a feature distribution guided deep MIL framework for WSI classification and positive patch localization. Instead of designing complex discriminative network architectures, we reveal that the inherent feature distribution of histopathological image data can serve as a very effective guide for instance classification. We propose a cluster-conditioned feature distribution modeling method and a pseudo label-based iterative feature space refinement strategy so that in the final feature space the positive and negative instances can be easily separated. Experiments on the CAMELYON16 dataset and the TCGA Lung Cancer dataset show that our method achieves new SOTA for both global classification and positive patch localization tasks.

Key facts. Cited by 40 papers in the map (data through 14 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 17 Jun 2022.

Builds on

13 of 26 references.

References · 13

  1. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  2. 2021Masked Autoencoders Are Scalable Vision Learnerscited by 7,403
  3. 2020Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Imagescited by 745
  4. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 555
  5. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 478
  6. 2018Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysiscited by 179
  7. 2019Deep neural network models for computational histopathology: A surveycited by 165
  8. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  9. 2021Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imagingcited by 31
  10. 2019Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Surveycited by 28
  11. 2018Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathologycited by 17
  12. 2019Multiple instance dense connected convolution neural network for aerial image scene classificationcited by 15
1 more
  1. 2019Multi-Instance Multi-Scale CNN for Medical Image Classificationcited by 13

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 12; 2025: 15; 2026: 5.

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  1. 2022Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classificationcited by 43
  2. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  3. 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
  4. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 22
  5. 2024Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classificationcited by 13
  6. 2024MamMIL: Multiple Instance Learning for Whole Slide Images with State Space Modelscited by 12
  7. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classificationcited by 12
  8. 2024FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classificationcited by 8
  9. 2024Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directionscited by 7
  10. 2023Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classificationcited by 6
  11. 2024Snuffy: Efficient Whole Slide Image Classifiercited by 6
  12. 2023TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classificationcited by 5
28 more
  1. 2023OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classificationcited by 5
  2. 2024Dataset Distillation for Histopathology Image Classificationcited by 4
  3. 2025Enhancing Zero-Shot Brain Tumor Subtype Classification via Fine-Grained Patch-Text Alignmentcited by 4
  4. 2023ProMIL: Probabilistic Multiple Instance Learning for Medical Imagingcited by 3
  5. 2024CIMIL-CRC: a clinically-informed multiple instance learning framework for patient-level colorectal cancer molecular subtypes classification from H&E stained imagescited by 3
  6. 2025Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modelingcited by 3
  7. 2025Contrastive Integrated Gradients: A Feature Attribution-Based Method for Explaining Whole Slide Image Classificationcited by 3
  8. 2024Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpointcited by 2
  9. 2025Learning from Majority Label: A Novel Problem in Multi-class Multiple-Instance Learningcited by 2
  10. 2025ME-Mamba: Multi-Expert Mamba with Efficient Knowledge Capture and Fusion for Multimodal Survival Analysiscited by 2
  11. 2024Multi-Scale Heterogeneity-Aware Hypergraph Representation for Histopathology Whole Slide Imagescited by 1
  12. 2025MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classificationcited by 1
  13. 2025GMAT: Grounded Multi-Agent Clinical Description Generation for Text Encoder in Vision-Language MIL for Whole Slide Image Classificationcited by 1
  14. 2025MAPLE: Multi-scale Attribute-enhanced Prompt Learning for Few-shot Whole Slide Image Classificationcited by 1
  15. 2024PreMix: Label-Efficient Multiple Instance Learning via Non-Contrastive Pre-training and Feature Mixingcited by 0
  16. 2024Efficient Classification of Histopathology Imagescited by 0
  17. 2025MsaMIL-Net: An End-to-End Multi-Scale Aware Multiple Instance Learning Network for Efficient Whole Slide Image Classificationcited by 0
  18. 2025Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervisioncited by 0
  19. 2025Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learningcited by 0
  20. 2025SemaMIL: Semantic-Aware Multiple Instance Learning with Retrieval-Guided State Space Modeling for Whole Slide Imagescited by 0
  21. 2025Revisiting Data Challenges of Computational Pathology: A Pack-based Multiple Instance Learning Training Frameworkcited by 0
  22. 2025EfficientMIL: Efficient Linear-Complexity MIL Method for WSI Classificationcited by 0
  23. 2025MeCaMIL: Causality-Aware Multiple Instance Learning for Fair and Interpretable Whole Slide Image Diagnosiscited by 0
  24. 2026PA-MIL: Phenotype-Aware Multiple Instance Learning Guided by Language Prompting and Genotype-to-Phenotype Relationshipscited by 0
  25. 2026MB-DSMIL-CL-PL: Scalable Weakly Supervised Ovarian Cancer Subtype Classification and Localisation Using Contrastive and Prototype Learning with Frozen Patch Featurescited by 0
  26. 2026Dictionary-based Pathology Mining with Hard-instance-assisted Classifier Debiasing for Genetic Biomarker Prediction from WSIscited by 0
  27. 2026MApLe: Multi-instance Alignment of Diagnostic Reports and Large Medical Imagescited by 0
  28. 2026Towards Whole-Study Screening for Congenital Heart Disease in Fetal Ultrasound Using Multiple Instance Learningcited by 0