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Found 3 papers, 2 papers with code
Multivariate Analysis on Performance Gaps of Artificial Intelligence Models in Screening Mammography
However, after controlling for confounding, we found lower FN risk associates with Other race(RR=0. 828;p=. 050), biopsy-proven benign lesions(RR=0. 927;p=. 011), and mass(RR=0. 921;p=. 010) or asymmetry(RR=0. 854;p=. 040); higher FN risk associates with architectural distortion (RR=1. 037;p<. 001).
GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.
Deep-LIBRA: Artificial intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment
Our method has been trained and validated on a multi-ethnic, multi-institutional dataset of 15, 661 images (4, 437 women), and then tested on an independent dataset of 6, 368 digital mammograms (1, 702 women; cases=414) for both PD estimation and discrimination of breast cancer.
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