mlsanityData to 7 Oct 2026

OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data

arXiv 2004.04742 ↗First posted 9 Apr 2020cs.CY eess.IVcited by 38

Not on the map. Why?

Abstract (arXiv)

A major barrier to medical imaging research and in particular the development of artificial intelligence (AI) is a lack of large databases of medical images which share images with other researchers. Without such databases it is not possible to train generalisable AI algorithms, and large amounts of time and funding is spent collecting smaller datasets at individual research centres. The OPTIMAM image database (OMI-DB) has been developed to overcome these barriers. OMI-DB consists of several relational databases and cloud storage systems, containing mammography images and associated clinical and pathological information. The database contains over 2.5 million images from 173,319 women collected from three UK breast screening centres. This includes 154,832 women with normal breasts, 6909 women with benign findings, 9690 women with screen-detected cancers and 1888 women with interval cancers. Collection is on-going and all women are followed-up and their clinical status updated according to subsequent screening episodes. The availability of prior screening mammograms and interval cancers is a vital resource for AI development. Data from OMI-DB has been shared with over 30 research groups and companies, since 2014. This progressive approach has been possible through sharing agreements between the funder and approved academic and commercial research groups. A research dataset such as the OMI-DB provides a powerful resource for research.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 10; 2023: 5; 2024: 8; 2025: 6; 2026: 7.

38 papers.

Sort:

By area:

  1. 2022VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammographycited by 83
  2. 2024Foundation Models in Radiology: What, How, When, Why and Why Notcited by 26
  3. 2024Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasetscited by 19
  4. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  5. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14
  6. 2022Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisationcited by 12
  7. 2022High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detectioncited by 11
  8. 2023MAM-E: Mammographic synthetic image generation with diffusion modelscited by 11
  9. 2022Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center studycited by 10
  10. 2023Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directionscited by 10
  11. 2022Recent Progress in Transformer-based Medical Image Analysiscited by 9
  12. 2023Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and dosescited by 8
26 more
  1. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  2. 2022Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classificationcited by 7
  3. 2024MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphscited by 6
  4. 2021Meta-repository of screening mammography classifierscited by 4
  5. 2024Full Field Digital Mammography Dataset from a Population Screening Programcited by 4
  6. 2022The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammogramscited by 3
  7. 2026Fatigue-Aware Learning to Defer via Constrained Optimisationcited by 3
  8. 2022A deep learning algorithm for reducing false positives in screening mammographycited by 2
  9. 2023M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detectorcited by 2
  10. 2022Deep is a Luxury We Don't Havecited by 1
  11. 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
  12. 2025T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Imagescited by 1
  13. 2025MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonizationcited by 1
  14. 2026Dataset-Origin Signatures and Shortcut Learning in Screening Mammography AI: A Cross-Dataset Case Studycited by 1
  15. 2023Problems and shortcuts in deep learning for screening mammographycited by 0
  16. 2024Siamese Networks with Soft Labels for Unsupervised Lesion Detection and Patch Pretraining on Screening Mammogramscited by 0
  17. 2024Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniquescited by 0
  18. 2024Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Modelcited by 0
  19. 2025DeCoDe: Defer-and-Complement Decision-Making via Decoupled Concept Bottleneck Modelscited by 0
  20. 2025MammoTracker: Mask-Guided Lesion Tracking in Temporal Mammogramscited by 0
  21. 2025MGRegBench: A Novel Benchmark Dataset with Anatomical Landmarks for Mammography Image Registrationcited by 0
  22. 2026MamaDino: A Hybrid Vision Model for Breast Cancer 3-Year Risk Predictioncited by 0
  23. 2026BAC-JEPA: Label-Efficient Breast Arterial Calcification Segmentation via Synthetic Mammography-Guided Supervisioncited by 0
  24. 2026Unsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasetscited by 0
  25. 2026Benchmarking the Robustness of Foundation Models for Mammography under Domain Shiftcited by 0
  26. 2026Modern Backbones Improve Multi-task DETR for Mammography Classification and Lesion Localizationcited by 0