2021/08/10 by Benjamin Stadnick, Stadnick, Benjamin, Jan Witowski +11 · 1 voice
Biochemistry, Genetics and Molecular Biology · Computer Science · Medicine · #AI in cancer detection #Biomedical Text Mining and Ontologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2108.04800
openalex publication_date 2021/08/10 · arxiv published 2021/08/10 · arxiv updated 2022/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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/mammographymetarepository.