mlsanityData to 7 Oct 2026

Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Images

arXiv 2303.06873 ↗First posted 13 Mar 2023cs.CVcited by 91

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Abstract (arXiv)

Multi-instance learning (MIL) is an effective paradigm for whole-slide pathological images (WSIs) classification to handle the gigapixel resolution and slide-level label. Prevailing MIL methods primarily focus on improving the feature extractor and aggregator. However, one deficiency of these methods is that the bag contextual prior may trick the model into capturing spurious correlations between bags and labels. This deficiency is a confounder that limits the performance of existing MIL methods. In this paper, we propose a novel scheme, Interventional Bag Multi-Instance Learning (IBMIL), to achieve deconfounded bag-level prediction. Unlike traditional likelihood-based strategies, the proposed scheme is based on the backdoor adjustment to achieve the interventional training, thus is capable of suppressing the bias caused by the bag contextual prior. Note that the principle of IBMIL is orthogonal to existing bag MIL methods. Therefore, IBMIL is able to bring consistent performance boosting to existing schemes, achieving new state-of-the-art performance. Code is available at https://github.com/HHHedo/IBMIL.

Key facts. Cited by 91 papers in the map (data through 7 Oct 2026). Builds on 27 of its 48 references that are in the map. First posted on arXiv 13 Mar 2023.

Builds on

27 of 48 references.

References · 27

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2021An Empirical Study of Training Self-Supervised Vision Transformerscited by 1,610
  3. 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
  4. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  5. 2018Attention-based Deep Multiple Instance Learningcited by 1,077
  6. 2021TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classificationcited by 549
  7. 2020Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learningcited by 473
  8. 2020Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effectcited by 256
  9. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  10. 2020Causal Intervention for Weakly-Supervised Semantic Segmentationcited by 229
  11. 2020Representation Learning via Invariant Causal Mechanismscited by 197
  12. 2020Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networkscited by 178
15 more
  1. 2015Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classificationcited by 159
  2. 2016Revisiting Multiple Instance Neural Networkscited by 119
  3. 2021Distilling Causal Effect of Data in Class-Incremental Learningcited by 117
  4. 2020Interventional Few-Shot Learningcited by 112
  5. 2020Visual Commonsense R-CNNcited by 106
  6. 2019CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentationcited by 73
  7. 2022Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet?cited by 67
  8. 2018Neural Image Compression for Gigapixel Histopathology Image Analysiscited by 65
  9. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  10. 2019Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Codingcited by 35
  11. 2022ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classificationcited by 30
  12. 2022Transformer based multiple instance learning for weakly supervised histopathology image segmentationcited by 26
  13. 2018Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysiscited by 17
  14. 2022Interventional Multi-Instance Learning with Deconfounded Instance-Level Predictioncited by 13
  15. 2017Label Stability in Multiple Instance Learningcited by 8

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Citing papers per year: 2023: 11; 2024: 32; 2025: 30; 2026: 18.

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  1. 2024Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathologycited by 70
  2. 2024Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathologycited by 66
  3. 2023Attention-Challenging Multiple Instance Learning for Whole Slide Image Classificationcited by 61
  4. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  5. 2025ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classificationcited by 28
  6. 2024Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interactioncited by 27
  7. 2024DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classificationcited by 25
  8. 2024Knowledge-enhanced Pretraining for Vision-language Pathology Foundation Model on Cancer Diagnosiscited by 21
  9. 2024FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classificationcited by 20
  10. 2023Rotation-Agnostic Image Representation Learning for Digital Pathologycited by 15
  11. 2024Knowledge-enhanced Visual-Language Pretraining for Computational Pathologycited by 14
  12. 2023Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational modelcited by 13
38 more
  1. 2024Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classificationcited by 13
  2. 2023Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classificationcited by 12
  3. 2024Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classificationcited by 12
  4. 2024Agent Aggregator with Mask Denoise Mechanism for Histopathology Whole Slide Image Analysiscited by 11
  5. 2024AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image Classificationcited by 10
  6. 2024Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classificationcited by 10
  7. 2024A New Era in Computational Pathology: A Survey on Foundation and Vision-Language Modelscited by 9
  8. 2025OTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transportcited by 8
  9. 2024From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classificationcited by 7
  10. 2024Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformercited by 7
  11. 2023Causal Clothes-Invariant Feature Learning for Cloth-Changing Person Re-IDcited by 6
  12. 2023Multi-level Multiple Instance Learning with Transformer for Whole Slide Image Classificationcited by 6
  13. 2023SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classificationcited by 6
  14. 2024A self-supervised framework for learning whole slide representationscited by 6
  15. 2024Prompting Whole Slide Image Based Genetic Biomarker Predictioncited by 6
  16. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  17. 2026ASMIL: Attention-Stabilized Multiple Instance Learning for Whole Slide Imagingcited by 5
  18. 2024CAMP: Continuous and Adaptive Learning Model in Pathologycited by 4
  19. 2024RankByGene: Gene-Guided Histopathology Representation Learning Through Cross-Modal Ranking Consistencycited by 4
  20. 2024Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancercited by 4
  21. 2023Data-driven Knowledge Fusion for Deep Multi-instance Learningcited by 3
  22. 2025MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for Few-Shot WSI Classificationcited by 3
  23. 2025Few-Shot Learning from Gigapixel Images via Hierarchical Vision-Language Alignment and Modelingcited by 3
  24. 2025Cracking Instance Jigsaw Puzzles: An Alternative to Multiple Instance Learning for Whole Slide Image Analysiscited by 3
  25. 2026Act Like a Pathologist: Tissue-Aware Whole Slide Image Reasoningcited by 3
  26. 2024Rethinking Attention-Based Multiple Instance Learning for Whole-Slide Pathological Image Classification: An Instance Attribute Viewpointcited by 2
  27. 2024From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformercited by 2
  28. 2025PathVQ: Reforming Computational Pathology Foundation Model for Whole Slide Image Analysis via Vector Quantizationcited by 2
  29. 2025PSA-MIL: A Probabilistic Spatial Attention-Based Multiple Instance Learning for Whole Slide Image Classificationcited by 2
  30. 2023MixUp-MIL: A Study on Linear & Multilinear Interpolation-Based Data Augmentation for Whole Slide Image Classificationcited by 1
  31. 2024Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labelscited by 1
  32. 2024Establishing Causal Relationship Between Whole Slide Image Predictions and Diagnostic Evidence Subregions in Deep Learningcited by 1
  33. 2024Rethinking Pre-Trained Feature Extractor Selection in Multiple Instance Learning for Whole Slide Image Classificationcited by 1
  34. 2024Beyond Labels: A Self-Supervised Framework with Masked Autoencoders and Random Cropping for Breast Cancer Subtype Classificationcited by 1
  35. 2024Advancing Histopathology with Deep Learning Under Data Scarcity: A Decade in Reviewcited by 1
  36. 2025MExD: An Expert-Infused Diffusion Model for Whole-Slide Image Classificationcited by 1
  37. 2025SMILE: a Scale-aware Multiple Instance Learning Method for Multicenter STAS Lung Cancer Histopathology Diagnosiscited by 1
  38. 2025Exploiting Low-Dimensional Manifold of Features for Few-Shot Whole Slide Image Classificationcited by 1

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