  Breast cancer is a leading cause of cancer-related deaths, but current
programs are expensive and prone to false positives, leading to unnecessary
follow-up and patient anxiety. This paper proposes a solution to automated
breast cancer detection, to improve the efficiency and accuracy of screening
programs. Different methodologies were tested against the RSNA dataset of
radiographic breast images of roughly 20,000 female patients and yielded an
average validation case pF1 score of 0.56 across methods.
