Journal
Scientific Reports
Publication Date
11-9-2024
Volume
14
Issue
1
First Page
27318
Document Type
Open Access Publication
DOI
10.1038/s41598-024-78648-0
Rights and Permissions
Liao, L., Aagaard, E.M. An open codebase for enhancing transparency in deep learning-based breast cancer diagnosis utilizing CBIS-DDSM data. Sci Rep 14, 27318 (2024). https://doi.org/10.1038/s41598-024-78648-0 This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Recommended Citation
Liao, Ling and Aagaard, Eva M, "An open codebase for enhancing transparency in deep learning-based breast cancer diagnosis utilizing CBIS-DDSM data." Scientific Reports. 14, 1. 27318 (2024).
https://digitalcommons.wustl.edu/oa_4/4500
Additional Links
Supplemental material is available for this article at publisher site.
