Screening Mammography Breast Cancer Detection
Debajyoti Chakraborty

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.
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Taxonomy
TopicsAI in cancer detection · Radiomics and Machine Learning in Medical Imaging · Digital Radiography and Breast Imaging
