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Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.

Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.

A detailed description of this dataset can be found in the following paper:

Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. Święcicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model. arXiv preprint arXiv:2011.07995.

Please reference this paper if you use this dataset. Please note that version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.

The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to, and (3) annotation boxes.

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

• Duke University Hospital/Duke University, Durham, NC, USA

• We would like to acknowledge all those who contributed to the curation of this dataset

• This work was supported by a grant from the NIH: 1 R01 EB021360 (PI: Mazurowski).