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. 2023 Feb 11;14:788. doi: 10.1038/s41467-023-36102-1

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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 5 — a The deep learning model trained on high-quality US images is adapted to low-quality pocket-size US images. b ROCs comparison of blind evaluation results of the junior radiologist. c ROCs comparison of blind evaluation results of the senior radiologist in diagnosing breast cancer. d Examples of typical case results. For lesions with different tumor depth and different benign and malignant types, the model can capture the elastic information effectively. Source data are provided as a Source Data file.