# Meta-repository of screening mammography classifiers

arXiv: https://arxiv.org/abs/2108.04800 · HTML: https://mlsanity.com/paper/2108.04800/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 10 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging](https://mlsanity.com/paper/1909.12475/) (2019)
- [Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening](https://mlsanity.com/paper/1903.08297/) (2019)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [A Step Toward Quantifying Independently Reproducible Machine Learning Research](https://mlsanity.com/paper/1909.06674/) (2019)
- [Detecting and classifying lesions in mammograms with Deep Learning](https://mlsanity.com/paper/1707.08401/) (2017)
- [An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization](https://mlsanity.com/paper/2002.07613/) (2020)
- [OPTIMAM Mammography Image Database: a large scale resource of mammography images and clinical data](https://mlsanity.com/paper/2004.04742/) (2020)
- [CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting](https://mlsanity.com/paper/2002.11379/) (2020)
- [Differences between human and machine perception in medical diagnosis](https://mlsanity.com/paper/2011.14036/) (2020)
- [Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis](https://mlsanity.com/paper/2106.07049/) (2021)
- [ModelHub.AI: Dissemination Platform for Deep Learning Models](https://mlsanity.com/paper/1911.13218/) (2019)
- [Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms](https://mlsanity.com/paper/2009.09282/) (2020)
- [Improving localization-based approaches for breast cancer screening exam classification](https://mlsanity.com/paper/1908.00615/) (2019)

## Built on by (most cited first, 4 of 4)

- [Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models](https://mlsanity.com/paper/2209.12420/) (2022)
- [Mammo-CLIP: Leveraging Contrastive Language-Image Pre-training (CLIP) for Enhanced Breast Cancer Diagnosis with Multi-view Mammography](https://mlsanity.com/paper/2404.15946/) (2024)
- [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)
- [Independent evaluation of state-of-the-art deep networks for mammography](https://mlsanity.com/paper/2206.12407/) (2022)

## Abstract (arXiv)

Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer diagnosis and reduce unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them. To enable reproducibility of research and to enable comparison between different methods, we release a meta-repository containing models for classification of screening mammograms. This meta-repository creates a framework that enables the evaluation of AI models on any screening mammography data set. At its inception, our meta-repository contains five state-of-the-art models with open-source implementations and cross-platform compatibility. We compare their performance on seven international data sets. Our framework has a flexible design that can be generalized to other medical image analysis tasks. The meta-repository is available at https://www.github.com/nyukat/mammography_metarepository.