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.
@article{arxiv.2108.04800,
title = {Meta-repository of screening mammography classifiers},
author = {Benjamin Stadnick and Jan Witowski and Vishwaesh Rajiv and Jakub Chłędowski and Farah E. Shamout and Kyunghyun Cho and Krzysztof J. Geras},
journal= {arXiv preprint arXiv:2108.04800},
year = {2022}
}
Comments
17 pages, 2 figures. Meta-repository available at https://www.github.com/nyukat/mammography_metarepository ; v3 adds results on the CSAW-CC dataset