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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

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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

   - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

  Continue with any names from additional submitting sites if collection consists of more that one

• 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).