Meta-repository of screening mammography classifiers
This provides a standardized framework for researchers and clinicians to compare AI models in breast cancer screening, though it is incremental as it aggregates existing methods.
The authors tackled the difficulty of comparing and reproducing AI models for breast cancer screening by releasing a meta-repository containing five state-of-the-art models for mammogram classification, enabling evaluation on seven international datasets.
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.