mlsanityData to 7 Oct 2026

Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding

arXiv 1910.10825 ↗First posted 23 Oct 2019cs.CV q-bio.TOcited by 35

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Abstract (arXiv)

Convolutional neural networks can be trained to perform histology slide classification using weak annotations with multiple instance learning (MIL). However, given the paucity of labeled histology data, direct application of MIL can easily suffer from overfitting and the network is unable to learn rich feature representations due to the weak supervisory signal. We propose to overcome such limitations with a two-stage semi-supervised approach that combines the power of data-efficient self-supervised feature learning via contrastive predictive coding (CPC) and the interpretability and flexibility of regularized attention-based MIL. We apply our two-stage CPC + MIL semi-supervised pipeline to the binary classification of breast cancer histology images. Across five random splits, we report state-of-the-art performance with a mean validation accuracy of 95% and an area under the ROC curve of 0.968. We further evaluate the quality of features learned via CPC relative to simple transfer learning and show that strong classification performance using CPC features can be efficiently leveraged under the MIL framework even with the feature encoder frozen.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Oct 2019.

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Citing papers per year: 2021: 11; 2022: 6; 2023: 12; 2024: 3; 2025: 3.

35 papers.

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  1. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  2. 2021Whole Slide Images are 2D Point Clouds: Context-Aware Survival Prediction using Patch-based Graph Convolutional Networkscited by 124
  3. 2023Interventional Bag Multi-Instance Learning On Whole-Slide Pathological Imagescited by 91
  4. 2021Extending the WILDS Benchmark for Unsupervised Adaptationcited by 85
  5. 2021Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classificationcited by 65
  6. 2021Multiple Instance Captioning: Learning Representations from Histopathology Textbooks and Articlescited by 58
  7. 2022Self-Supervised Vision Transformers Learn Visual Concepts in Histopathologycited by 49
  8. 2023The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classificationcited by 41
  9. 2021Self-supervised driven consistency training for annotation efficient histopathology image analysiscited by 39
  10. 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
  11. 2021DeepSMILE: Contrastive self-supervised pre-training benefits MSI and HRD classification directly from H&E whole-slide images in colorectal and breast cancercited by 27
  12. 2023Diagnose Like a Pathologist: Transformer-Enabled Hierarchical Attention-Guided Multiple Instance Learning for Whole Slide Image Classificationcited by 27
23 more
  1. 2024DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image Classificationcited by 25
  2. 2021Self supervised learning improves dMMR/MSI detection from histology slides across multiple cancerscited by 23
  3. 2023Rethinking Multiple Instance Learning for Whole Slide Image Classification: A Good Instance Classifier is All You Needcited by 21
  4. 2021Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applicationscited by 12
  5. 2023Hierarchical discriminative learning improves visual representations of biomedical microscopycited by 12
  6. 2023Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learningcited by 7
  7. 2024Advances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directionscited by 7
  8. 2025Foundation Models in Medical Imaging: A Review and Outlookcited by 7
  9. 2023Data efficient deep learning for medical image analysis: A surveycited by 6
  10. 2023SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classificationcited by 6
  11. 2025Self-Supervision Enhances Instance-based Multiple Instance Learning Methods in Digital Pathology: A Benchmark Studycited by 6
  12. 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
  13. 2022Interpretable Prediction of Lung Squamous Cell Carcinoma Recurrence With Self-supervised Learningcited by 5
  14. 2023A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Imagescited by 5
  15. 2023TPMIL: Trainable Prototype Enhanced Multiple Instance Learning for Whole Slide Image Classificationcited by 5
  16. 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
  17. 2023Context-Aware Self-Supervised Learning of Whole Slide Imagescited by 4
  18. 2021Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learningcited by 3
  19. 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
  20. 2022HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learningcited by 2
  21. 2023Explainable and Position-Aware Learning in Digital Pathologycited by 0
  22. 2024Smooth InfoMax -- Towards Easier Post-Hoc Interpretabilitycited by 0
  23. 2025Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervisioncited by 0