mlsanityData to 7 Oct 2026

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

arXiv 1612.05968 ↗First posted 18 Dec 2016cs.CV cs.LGcited by 32

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Abstract (arXiv)

Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional features for natural image analysis and multi-instance learning for labeling a set of instances/patches, we propose end-to-end trained deep multi-instance networks for mass classification based on whole mammogram without the aforementioned costly need to annotate the training data. We explore three different schemes to construct deep multi-instance networks for whole mammogram classification. Experimental results on the INbreast dataset demonstrate the robustness of proposed deep networks compared to previous work using segmentation and detection annotations in the training.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Dec 2016.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 11; 2024: 3; 2025: 3; 2026: 5.

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  1. 2022DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classificationcited by 254
  2. 2021Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slidescited by 43
  3. 2024xMIL: Insightful Explanations for Multiple Instance Learning in Histopathologycited by 15
  4. 2022Stabilizing Adversarially Learned One-Class Novelty Detection Using Pseudo Anomaliescited by 14
  5. 2023Label-Efficient Deep Learning in Medical Image Analysis: Challenges and Future Directionscited by 14
  6. 2023Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classificationcited by 12
  7. 2022Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Modelscited by 11
  8. 2022Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center studycited by 10
  9. 2022Self-Supervised Equivariant Regularization Reconciles Multiple Instance Learning: Joint Referable Diabetic Retinopathy Classification and Lesion Segmentationcited by 10
  10. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 10
  11. 2021Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosiscited by 7
  12. 2022EGFR Mutation Prediction of Lung Biopsy Images using Deep Learningcited by 7
20 more
  1. 2023MammoDG: Generalisable Deep Learning Breaks the Limits of Cross-Domain Multi-Center Breast Cancer Screeningcited by 7
  2. 2022All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classificationcited by 6
  3. 2023SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classificationcited by 6
  4. 2023Dual-view Correlation Hybrid Attention Network for Robust Holistic Mammogram Classificationcited by 5
  5. 2025Fast and Accurate Gigapixel Pathological Image Classification with Hierarchical Distillation Multi-Instance Learningcited by 5
  6. 2021Multi-task fusion for improving mammography screening data classificationcited by 4
  7. 2023Unsupervised Mutual Transformer Learning for Multi-Gigapixel Whole Slide Image Classificationcited by 3
  8. 2024VALD-MD: Visual Attribution via Latent Diffusion for Medical Diagnosticscited by 2
  9. 2025Continual Multiple Instance Learning with Enhanced Localization for Histopathological Whole Slide Image Analysiscited by 2
  10. 2023Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methodscited by 1
  11. 2023Case-level Breast Cancer Prediction for Real Hospital Settingscited by 1
  12. 2023Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological imagescited by 1
  13. 2024Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactualscited by 1
  14. 2025Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learningcited by 1
  15. 2026A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classificationcited by 1
  16. 2023Multiple Instance Learning for Uplift Modelingcited by 0
  17. 2026Evaluating Federated Learning approaches for mammography under breast density heterogeneitycited by 0
  18. 2026Clinically-Grounded Counterfactual Reasoning for Medical Video Diagnosiscited by 0
  19. 2026Normal Guidance is what Attention Needscited by 0
  20. 2026BiLoG-Net: A Bi-Context Location-Guided Network for Breast Mass Segmentation and Malignancy Classification in Mammographycited by 0