Full Field Digital Mammography Dataset from a Population Screening Program

TL;DR

This paper introduces NL-Breast-Screening, a large, publicly available mammography dataset from a Canadian screening program, aimed at advancing automated detection methods for early breast cancer screening.

Contribution

It provides a new, extensive dataset specifically designed for developing and testing automated breast cancer screening algorithms in population screening contexts.

Findings

Dataset includes 5997 biopsy-confirmed exams with four views each.

Contains cases with false-positive radiologist readings.

Aims to facilitate development of automated screening tools.

Abstract

Breast cancer presents the second largest cancer risk in the world to women. Early detection of cancer has been shown to be effective in reducing mortality. Population screening programs schedule regular mammography imaging for participants, promoting early detection. Currently, such screening programs require manual reading. False-positive errors in the reading process unnecessarily leads to costly follow-up and patient anxiety. Automated methods promise to provide more efficient, consistent and effective reading. To facilitate their development, a number of datasets have been created. With the aim of specifically targeting population screening programs, we introduce NL-Breast-Screening, a dataset from a Canadian provincial screening program. The dataset consists of 5997 mammography exams, each of which has four standard views and is biopsy-confirmed. Cases where radiologist reading was a false-positive are identified. NL-Breast is made publicly available as a new resource to promote advances in automation for population screening programs.