{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T12:01:10Z","timestamp":1775217670102,"version":"3.50.1"},"reference-count":20,"publisher":"Institution of Engineering and Technology (IET)","issue":"1","license":[{"start":{"date-parts":[[2025,5,4]],"date-time":"2025-05-04T00:00:00Z","timestamp":1746316800000},"content-version":"vor","delay-in-days":123,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ietresearch.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["IET Image Processing"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Breast cancer is commonly diagnosed through ultrasound imaging as a primary method in clinical practice. However, the lack of large annotated datasets for breast ultrasound images, along with issues such as inconsistent edge and conditional distributions across different datasets, poses significant challenges to both manual and AI\u2010assisted diagnosis. To address these issues, this paper proposes a dynamic adversarial domain adaptation model based on Wasserstein distance (EMD_DAAN). The EMD_DAAN model enhances the existing dynamic adversarial domain adaptation framework by incorporating an adaptive layer, further aligning the feature distributions between the source and target domain datasets. The Wasserstein distance is employed to optimize this adaptive layer, minimizing the distributional discrepancy between the feature spaces of the two domains by constructing the least\u2010cost transport path. This approach improves the model's cross\u2010domain generalization ability and robustness to noise interference. Through dual feature alignment via the adaptive layer and adversarial learning, the model's classification performance on breast ultrasound images is significantly enhanced. Experimental results demonstrate that the EMD_DAAN model achieves an accuracy of 82.75% on breast ultrasound images, substantially outperforming typical adversarial domain adaptation models such as DAAN in terms of classification performance.<\/jats:p>","DOI":"10.1049\/ipr2.70096","type":"journal-article","created":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T00:14:36Z","timestamp":1746404076000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EMD\u2010DAAN: A Wasserstein Distance\u2010Based Dynamic Adversarial Domain Adaptation Network Model for Breast Ultrasound Image Classification"],"prefix":"10.1049","volume":"19","author":[{"given":"Ying","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Ultrasound The First Affiliated Hospital of Jinan University  Guangzhou Guangdong China"},{"name":"Department of Ultrasound Chaoshan Hospital the First Affiliated Hospital of Jinan University  Chaozhou Guangdong China"}]},{"given":"Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Guangdong University of Finance and Economics  Guangzhou Guangdong China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4763-5321","authenticated-orcid":false,"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Guangdong University of Finance and Economics  Guangzhou Guangdong China"}]}],"member":"265","published-online":{"date-parts":[[2025,5,4]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.currproblcancer.2023.100967"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.32604\/cmes.2023.025484"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2024.3357877"},{"key":"e_1_2_11_5_1","first-page":"357","article-title":"Classification and Diagnosis of Ultrasound Images With Breast Tumors Based on Transfer Learning","author":"WU Y.","year":"2019","journal-title":"Chinese Journal of Medical Imaging Technology"},{"issue":"59","key":"e_1_2_11_6_1","first-page":"1","article-title":"Domain\u2010Adversarial Training of Neural Networks","volume":"17","author":"Ganin Y.","year":"2016","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.117978"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.20965\/jaciii.2024.p0835"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2021.09.002"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2022.07.006"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2022.106891"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106018"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2020.01.001"},{"key":"e_1_2_11_14_1","article-title":"Impact of Ultrasound Image Reconstruction Method on Breast Lesion Classification With Neural Transfer Learning","author":"Byra M.","year":"2018","journal-title":"arXiv:1804.02119"},{"key":"e_1_2_11_15_1","doi-asserted-by":"crossref","unstructured":"A.Hijab M. 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M.Gomaa andA.Eldeib \u201cBreast Cancer Classification in Ultrasound Images Using Transfer Learning \u201d in2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME)(IEEE 2019) 1\u20134.","DOI":"10.1109\/ICABME47164.2019.8940291"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics12010135"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22030807"},{"key":"e_1_2_11_18_1","doi-asserted-by":"crossref","unstructured":"W.Wang Y.Li X.Yan M.Xiao andM.Gao \u201cBreast Cancer Image Classification Method Based on Deep Transfer Learning \u201d inProceedings of the International Conference on Image Processing Machine Learning and Pattern Recognition(IEEE 2024) 190\u2013197.","DOI":"10.1145\/3700906.3700937"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107914"},{"key":"e_1_2_11_20_1","doi-asserted-by":"crossref","unstructured":"B.SunandK.Saenko \u201cDeep Coral: Correlation Alignment for Deep Domain Adaptation \u201d inEuropean Conference on Computer Vision(Springer 2016) 443\u2013450.","DOI":"10.1007\/978-3-319-49409-8_35"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2017.2731873"}],"container-title":["IET Image Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70096","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/full-xml\/10.1049\/ipr2.70096","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/pdf\/10.1049\/ipr2.70096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T11:19:41Z","timestamp":1775215181000},"score":1,"resource":{"primary":{"URL":"https:\/\/ietresearch.onlinelibrary.wiley.com\/doi\/10.1049\/ipr2.70096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":20,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1049\/ipr2.70096"],"URL":"https:\/\/doi.org\/10.1049\/ipr2.70096","archive":["Portico"],"relation":{},"ISSN":["1751-9659","1751-9667"],"issn-type":[{"value":"1751-9659","type":"print"},{"value":"1751-9667","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2024-10-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-14","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-05-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70096"}}