mlsanityData to 7 Oct 2026

VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography

arXiv 2203.11205 ↗First posted 20 Mar 2022eess.IV cs.CVcited by 83

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Abstract (arXiv)

Mammography, or breast X-ray, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe or CADx) tools have been developed to support physicians and improve the accuracy of interpreting mammography. However, most published datasets of mammography are either limited on sample size or digitalized from screen-film mammography (SFM), hindering the development of CADe and CADx tools which are developed based on full-field digital mammography (FFDM). To overcome this challenge, we introduce VinDr-Mammo - a new benchmark dataset of FFDM for detecting and diagnosing breast cancer and other diseases in mammography. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. It is created for the assessment of Breast Imaging Reporting and Data System (BI-RADS) and density at the breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available on PhysioNet as a new imaging resource to promote advances in developing CADe and CADx tools for breast cancer screening.

Key facts. Cited by 83 papers in the map (data through 7 Oct 2026). Builds on 3 of its 33 references that are in the map. First posted on arXiv 20 Mar 2022.

Builds on

3 of 33 references.

References · 3

  1. 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
  2. 2019Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screeningcited by 94
  3. 2020OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical datacited by 38

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 14; 2024: 19; 2025: 29; 2026: 20.

83 papers.

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  1. 2023Towards Generalist Biomedical AIcited by 286
  2. 2023Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Datacited by 176
  3. 2024Mammo-CLIP: A Vision Language Foundation Model to Enhance Data Efficiency and Robustness in Mammographycited by 35
  4. 2024MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language Modelscited by 28
  5. 2024Interpretable Bilingual Multimodal Large Language Model for Diverse Biomedical Taskscited by 24
  6. 2025MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoderscited by 20
  7. 2023Large-scale Long-tailed Disease Diagnosis on Radiology Imagescited by 17
  8. 2024MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generationcited by 17
  9. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  10. 2024Robust image representations with counterfactual contrastive learningcited by 12
  11. 2023MAM-E: Mammographic synthetic image generation with diffusion modelscited by 11
  12. 2024MV-Swin-T: Mammogram Classification with Multi-view Swin Transformercited by 11
38 more
  1. 2023Towards Robust Natural-Looking Mammography Lesion Synthesis on Ipsilateral Dual-Views Breast Cancer Analysiscited by 10
  2. 2023Delving into Ipsilateral Mammogram Assessment under Multi-View Networkcited by 8
  3. 2025MedSG-Bench: A Benchmark for Medical Image Sequences Groundingcited by 8
  4. 2022An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiographcited by 7
  5. 2023K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imagingcited by 6
  6. 2024RadiomicsFill-Mammo: Synthetic Mammogram Mass Manipulation with Radiomics Featurescited by 5
  7. 2025Mammo-FM: Breast-specific foundational model for Integrated Mammographic Diagnosis, Prognosis, and Reportingcited by 5
  8. 2023In-context Cross-Density Adaptation on Noisy Mammogram Abnormalities Detectioncited by 4
  9. 2024Full Field Digital Mammography Dataset from a Population Screening Programcited by 4
  10. 2023DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learningcited by 3
  11. 2024Prototype-based Interpretable Breast Cancer Prediction Models: Analysis and Challengescited by 3
  12. 2024ConPro: Learning Severity Representation for Medical Images using Contrastive Learning and Preference Optimizationcited by 3
  13. 2024Transferring Domain Knowledge with (X)AI-Based Learning Systemscited by 3
  14. 2025Trustworthy image-to-image translation: evaluating uncertainty calibration in unpaired training scenarioscited by 3
  15. 2025Bias and Generalizability of Foundation Models across Datasets in Breast Mammographycited by 3
  16. 2023BenchMD: A Benchmark for Unified Learning on Medical Images and Sensorscited by 2
  17. 2023TransReg: Cross-transformer as auto-registration module for multi-view mammogram mass detectioncited by 2
  18. 2025MedicalNarratives: Connecting Medical Vision and Language with Localized Narrativescited by 2
  19. 2025Multi-Modal Explainable Medical AI Assistant for Trustworthy Human-AI Collaborationcited by 2
  20. 2025Mammo-SAE: Interpreting Breast Cancer Concept Learning with Sparse Autoencoderscited by 2
  21. 2025A Hybrid CNN-VSSM model for Multi-View, Multi-Task Mammography Analysis: Robust Diagnosis with Attention-Based Fusioncited by 2
  22. 2025Exemplar Med-DETR: Toward Generalized and Robust Lesion Detection in Mammogram Images and beyondcited by 2
  23. 2023Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigationcited by 1
  24. 2023Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methodscited by 1
  25. 2023Case-level Breast Cancer Prediction for Real Hospital Settingscited by 1
  26. 2024Attention-Guided Erasing: A Novel Augmentation Method for Enhancing Downstream Breast Density Classificationcited by 1
  27. 2024Mammo-Clustering: A Multi-views Tri-level Information Fusion Context Clustering Framework for Localization and Classification in Mammographycited by 1
  28. 2024MamT4: Multi-view Attention Networks for Mammography Cancer Classificationcited by 1
  29. 2025Semise: Semi-supervised learning for severity representation in medical imagecited by 1
  30. 2025Style transfer as data augmentation: evaluating unpaired image-to-image translation models in mammographycited by 1
  31. 2025Can Score-Based Generative Modeling Effectively Handle Medical Image Classification?cited by 1
  32. 2025A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Studycited by 1
  33. 2025T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Imagescited by 1
  34. 2025Joint Holistic and Lesion Controllable Mammogram Synthesis via Gated Conditional Diffusion Modelcited by 1
  35. 2025Bidirectional Mammogram View Translation with Column-Aware and Implicit 3D Conditional Diffusioncited by 1
  36. 2025MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonizationcited by 1
  37. 2026MIL-PF: Multiple Instance Learning on Precomputed Features for Mammography Classificationcited by 1
  38. 2026Attend what matters: Leveraging vision foundational models for breast cancer classification using mammogramscited by 1

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