# OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data arXiv: https://arxiv.org/abs/2004.04742 ยท HTML: https://mlsanity.com/paper/2004.04742/ ## Key facts - Cited by 38 papers in the map (data through 14 Oct 2026). - First posted on arXiv 9 Apr 2020. ## Built on by (most cited first, 30 of 38) - [VinDr-Mammo: A large-scale benchmark dataset for computer-aided diagnosis in full-field digital mammography](https://mlsanity.com/paper/2203.11205/) (2022) - [Foundation Models in Radiology: What, How, When, Why and Why Not](https://mlsanity.com/paper/2411.18730/) (2024) - [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024) - [Data synthesis and adversarial networks: A review and meta-analysis in cancer imaging](https://mlsanity.com/paper/2107.09543/) (2021) - [medigan: a Python library of pretrained generative models for medical image synthesis](https://mlsanity.com/paper/2209.14472/) (2022) - [Multi-view Local Co-occurrence and Global Consistency Learning Improve Mammogram Classification Generalisation](https://mlsanity.com/paper/2209.10478/) (2022) - [High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection](https://mlsanity.com/paper/2209.09809/) (2022) - [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023) - [MAM-E: Mammographic synthetic image generation with diffusion models](https://mlsanity.com/paper/2311.09822/) (2023) - [Domain generalization in deep learning-based mass detection in mammography: A large-scale multi-center study](https://mlsanity.com/paper/2201.11620/) (2022) - [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022) - [Knowledge-based in silico models and dataset for the comparative evaluation of mammography AI for a range of breast characteristics, lesion conspicuities and doses](https://mlsanity.com/paper/2310.18494/) (2023) - [A Systematic Review of Generalization Research in Medical Image Classification](https://mlsanity.com/paper/2403.12167/) (2024) - [Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification](https://mlsanity.com/paper/2203.04961/) (2022) - [MedPix 2.0: A Comprehensive Multimodal Biomedical Data set for Advanced AI Applications with Retrieval Augmented Generation and Knowledge Graphs](https://mlsanity.com/paper/2407.02994/) (2024) - [Meta-repository of screening mammography classifiers](https://mlsanity.com/paper/2108.04800/) (2021) - [Full Field Digital Mammography Dataset from a Population Screening Program](https://mlsanity.com/paper/2411.02710/) (2024) - [The EMory BrEast imaging Dataset (EMBED): A Racially Diverse, Granular Dataset of 3.5M Screening and Diagnostic Mammograms](https://mlsanity.com/paper/2202.04073/) (2022) - [Fatigue-Aware Learning to Defer via Constrained Optimisation](https://mlsanity.com/paper/2604.00904/) (2026) - [A deep learning algorithm for reducing false positives in screening mammography](https://mlsanity.com/paper/2204.06671/) (2022) - [M&M: Tackling False Positives in Mammography with a Multi-view and Multi-instance Learning Sparse Detector](https://mlsanity.com/paper/2308.06420/) (2023) - [Deep is a Luxury We Don't Have](https://mlsanity.com/paper/2208.06066/) (2022) - [A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study](https://mlsanity.com/paper/2504.05636/) (2025) - [T-SYNTH: A Knowledge-Based Dataset of Synthetic Breast Images](https://mlsanity.com/paper/2507.04038/) (2025) - [MammoClean: Toward Reproducible and Bias-Aware AI in Mammography through Dataset Harmonization](https://mlsanity.com/paper/2511.02400/) (2025) - [Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift](https://mlsanity.com/paper/2607.10358/) (2026) - [Dataset-Origin Signatures and Shortcut Learning in Screening Mammography AI: A Cross-Dataset Case Study](https://mlsanity.com/paper/2607.15416/) (2026) - [Problems and shortcuts in deep learning for screening mammography](https://mlsanity.com/paper/2303.16417/) (2023) - [Siamese Networks with Soft Labels for Unsupervised Lesion Detection and Patch Pretraining on Screening Mammograms](https://mlsanity.com/paper/2401.05570/) (2024) - [Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques](https://mlsanity.com/paper/2406.00532/) (2024) ## 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.