...
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).
Localtab Group |
---|
Localtab |
---|
active | true |
---|
title | Data Access |
---|
| Data AccessClick the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever . Click the Search button to open our Data Portal, where you can browse the data collection and/or download a subset of its contents. Data Type | Download all or Query/Filter |
---|
Images (DICOM, XX.X GB) DBT | Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/pages/editattachment.action?pageId=64685580&fileName=DBT-Challenge-Train.TCIA |
---|
| |
(Search button will not work until the data is ready to be released) | Annotations (csv) | Tcia button generator |
---|
url | https://wiki.cancerimagingarchive.net/pages/editattachment.action?pageId=64685580&fileName=Duke+Breast+DBT+boxes-train.csv |
---|
| |
| Labels (csv) | | Click the Versions tab for more info about data releases. |
Localtab |
---|
title | Detailed Description |
---|
| Detailed Description | |
---|
Modalities | DBT | Number of Participants | 985 | Number of Studies | 1000 | Number of Series | 3592 | Number of Images | 3592 | Images Size (TB, compressed) | 1.2 |
|
Localtab |
---|
title | Citations & Data Usage Policy |
---|
| Citations & Data Usage PolicyAdd any special restrictions in here. Tcia license 4 international |
---|
Info |
---|
| DOI goes here. Create using pubhub with information from Collection Approval form |
Info |
---|
title | Publication Citation |
---|
| Buda, A., Saha, R., Walsh, S., Ghate, N., Li, A., Święcicki, J. Y., Lo, M. A., Mazurowski, M., 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 https://arxiv.org/abs/2011.07995. |
Info |
---|
| Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
Localtab |
---|
| Version 1 (Current): Updated 2020/mm/dd Data Type | Download all or Query/Filter |
---|
Images (DICOM, xx.x GB) | | Annotations (CSV) | | Labels (CSV) | |
|
|