Screening Mammography Breast Cancer Detection

TL;DR

This paper explores automated breast cancer detection methods to enhance screening accuracy and efficiency, tested on a large dataset with promising validation scores, aiming to reduce false positives and improve patient outcomes.

Contribution

It introduces and evaluates multiple automated detection methodologies on a large dataset, demonstrating potential improvements over current screening practices.

Findings

Average validation pF1 score of 0.56 across methods

Multiple methodologies tested on RSNA dataset

Potential to reduce false positives in screening

Abstract

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.