Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, several recent studies show that AI has the potential to improve radiologists accuracy, subsequently helping in early cancer diagnosis and reducing unnecessary workup. As the number of proposed models and their complexity grows, it is becoming increasingly difficult to re-implement them in order to reproduce the results and to compare different approaches. To enable reproducibility of research in this application area and to enable comparison between different methods, we release a meta-repository containing deep learning models for classification of screening mammograms. This meta-repository creates a framework that enables the evaluation of machine learning models on any private or public 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 six international data sets: two New York University breast cancer screening data sets, DDSM, INbreast, OPTIMAM and Chinese Mammography Database. 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.
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