A curated mammography data set for use in computer-aided detection and diagnosis research

TLDR

Published research on mammography CAD systems is hard to replicate because no standard evaluation dataset exists, forcing reliance on private or unspecified public subsets that prevent direct performance comparison. This work addresses that gap by releasing a standardized, updated version of the Digital Database for Screening Mammography for future CADx and CADe research. The CBIS‑DDSM dataset provides decompressed images, curated by trained mammographers, with updated mass segmentation, bounding boxes, and pathology labels, comprising 753 calcification and 891 mass cases formatted like modern computer‑vision datasets.

Abstract

Abstract Published research results are difficult to replicate due to the lack of a standard evaluation data set in the area of decision support systems in mammography; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. This causes an inability to directly compare the performance of methods or to replicate prior results. We seek to resolve this substantial challenge by releasing an updated and standardized version of the Digital Database for Screening Mammography (DDSM) for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography. Our data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets. The data set contains 753 calcification cases and 891 mass cases, providing a data-set size capable of analyzing decision support systems in mammography.