  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.
