{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T16:12:39Z","timestamp":1769098359650,"version":"3.49.0"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T00:00:00Z","timestamp":1747440000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T00:00:00Z","timestamp":1747440000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271465"],"award-info":[{"award-number":["62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271465"],"award-info":[{"award-number":["62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271465"],"award-info":[{"award-number":["62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271465"],"award-info":[{"award-number":["62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271465"],"award-info":[{"award-number":["62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271465"],"award-info":[{"award-number":["62271465"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Suzhou Basic Research Program","award":["SYG202338"],"award-info":[{"award-number":["SYG202338"]}]},{"name":"Suzhou Basic Research Program","award":["SYG202338"],"award-info":[{"award-number":["SYG202338"]}]},{"name":"Suzhou Basic Research Program","award":["SYG202338"],"award-info":[{"award-number":["SYG202338"]}]},{"name":"Suzhou Basic Research Program","award":["SYG202338"],"award-info":[{"award-number":["SYG202338"]}]},{"name":"Suzhou Basic Research Program","award":["SYG202338"],"award-info":[{"award-number":["SYG202338"]}]},{"name":"Suzhou Basic Research Program","award":["SYG202338"],"award-info":[{"award-number":["SYG202338"]}]},{"name":"Open Fund Project of Guangdong Academy of Medical Sciences, China","award":["YKY-KF202206"],"award-info":[{"award-number":["YKY-KF202206"]}]},{"name":"Open Fund Project of Guangdong Academy of Medical Sciences, China","award":["YKY-KF202206"],"award-info":[{"award-number":["YKY-KF202206"]}]},{"name":"Open Fund Project of Guangdong Academy of Medical Sciences, China","award":["YKY-KF202206"],"award-info":[{"award-number":["YKY-KF202206"]}]},{"name":"Open Fund Project of Guangdong Academy of Medical Sciences, China","award":["YKY-KF202206"],"award-info":[{"award-number":["YKY-KF202206"]}]},{"name":"Open Fund Project of Guangdong Academy of Medical Sciences, China","award":["YKY-KF202206"],"award-info":[{"award-number":["YKY-KF202206"]}]},{"name":"Open Fund Project of Guangdong Academy of Medical Sciences, China","award":["YKY-KF202206"],"award-info":[{"award-number":["YKY-KF202206"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2033"],"award-info":[{"award-number":["U22A2033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U22A2033"],"award-info":[{"award-number":["U22A2033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MH290"],"award-info":[{"award-number":["ZR2020MH290"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MH290"],"award-info":[{"award-number":["ZR2020MH290"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"DOI":"10.1038\/s41746-025-01641-y","type":"journal-article","created":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T11:15:13Z","timestamp":1747480513000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Fair ultrasound diagnosis via adversarial protected attribute aware perturbations on latent embeddings"],"prefix":"10.1038","volume":"8","author":[{"given":"Zikang","family":"Xu","sequence":"first","affiliation":[]},{"given":"Fenghe","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Quan","family":"Quan","sequence":"additional","affiliation":[]},{"given":"Qingsong","family":"Yao","sequence":"additional","affiliation":[]},{"given":"Qingpeng","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Jianrui","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Chunping","family":"Ning","sequence":"additional","affiliation":[]},{"given":"S. Kevin","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,17]]},"reference":[{"key":"1641_CR1","doi-asserted-by":"crossref","unstructured":"Zhou, S.K. et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises. In: Proc. of the IEEE (2021).","DOI":"10.1109\/JPROC.2021.3054390"},{"key":"1641_CR2","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2019.2932116","volume":"108","author":"RJ Van Sloun","year":"2019","unstructured":"Van Sloun, R. J., Cohen, R. & Eldar, Y. C. Deep learning in ultrasound imaging. Proc. IEEE 108, 11\u201329 (2019).","journal-title":"Proc. IEEE"},{"key":"1641_CR3","doi-asserted-by":"publisher","first-page":"103202","DOI":"10.1016\/j.media.2024.103202","volume":"96","author":"J Jiao","year":"2024","unstructured":"Jiao, J. et al. USFM: a universal ultrasound foundation model generalized to tasks and organs towards label efficient image analysis. Med. Image Anal. 96, 103202 (2024).","journal-title":"Med. Image Anal."},{"key":"1641_CR4","doi-asserted-by":"crossref","unstructured":"Booth, B. M. et al. Bias and fairness in multimodal machine learning: a case study of automated video interviews. In: Proc. 2021 Int. Conf. Multimodal Interact. (ICMI 2021), 268\u2013277 (2021).","DOI":"10.1145\/3462244.3479897"},{"key":"1641_CR5","doi-asserted-by":"crossref","unstructured":"Jin, R., Deng, W., Chen, M. & Li, X. Debiased noise editing on foundation models for fair medical image classification. Int. Conf. Med. Image Comput. Comput.-Assist. Interv. (MICCAI 2024), 164\u2013174 (Springer, 2024).","DOI":"10.1007\/978-3-031-72117-5_16"},{"key":"1641_CR6","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01276-5","volume":"7","author":"Z Xu","year":"2024","unstructured":"Xu, Z. et al. Addressing fairness issues in deep learning-based medical image analysis: a systematic review. npj Digit. Med. 7, 286 (2024).","journal-title":"npj Digit. Med."},{"key":"1641_CR7","doi-asserted-by":"publisher","first-page":"e009473","DOI":"10.1161\/CIRCHEARTFAILURE.122.009473","volume":"15","author":"Y Li","year":"2022","unstructured":"Li, Y., Wang, H. & Luo, Y. Improving fairness in the prediction of heart failure length of stay and mortality by integrating social determinants of health. Circ. Heart Fail. 15, e009473 (2022).","journal-title":"Circ. Heart Fail."},{"key":"1641_CR8","doi-asserted-by":"crossref","unstructured":"Puyol-Ant\u00f3n, E. et al. Fairness in cardiac MR image analysis: an investigation of bias due to data imbalance in deep learning based segmentation. Med. Image Comput. Comput. Assist. Interv. MICCAI 2021: 24th Int. Conf., Strasbourg, France, Sept. 27-Oct. 1, 2021, Proc., Part III, 413\u2013423 (Springer, 2021).","DOI":"10.1007\/978-3-030-87199-4_39"},{"key":"1641_CR9","doi-asserted-by":"publisher","first-page":"102723","DOI":"10.1016\/j.media.2022.102723","volume":"84","author":"G Pombo","year":"2023","unstructured":"Pombo, G. et al. Equitable modelling of brain imaging by counterfactual augmentation with morphologically constrained 3d deep generative models. Med. Image Anal. 84, 102723 (2023).","journal-title":"Med. Image Anal."},{"key":"1641_CR10","doi-asserted-by":"crossref","unstructured":"Oguguo, T. et al. A comparative study of fairness in medical machine learning. 2023 IEEE 20th Int. Symp. Biomed. Imaging (ISBI 2023), 1\u20135 (IEEE, 2023).","DOI":"10.1109\/ISBI53787.2023.10230368"},{"key":"1641_CR11","doi-asserted-by":"crossref","unstructured":"Stanley, E. A., Wilms, M. & Forkert, N. D. Disproportionate subgroup impacts and other challenges of fairness in artificial intelligence for medical image analysis. Workshop on the Ethical and Philosophical Issues in Medical Imaging 14\u201325 (Springer, 2022).","DOI":"10.1007\/978-3-031-23223-7_2"},{"key":"1641_CR12","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zeng, D., Xu, X., Shi, Y. & Hu, J. Fairprune: achieving fairness through pruning for dermatological disease diagnosis. Intenational Conference Medical Image Computing and Computer Assisted Intervention. (MICCAI 2022), 743\u2013753 (Springer, 2022).","DOI":"10.1007\/978-3-031-16431-6_70"},{"key":"1641_CR13","doi-asserted-by":"crossref","unstructured":"Xiao, C. et al. Generating Adversarial Examples with Adversarial Networks. In: Proc. of the Twenty-Seventh International Joint Conference on Artificial Intelligence Main Track. (IJCAI-18), 3905\u20133911 (International Joint Conferences on Artificial Intelligence Organization, 2018).","DOI":"10.24963\/ijcai.2018\/543"},{"key":"1641_CR14","doi-asserted-by":"crossref","unstructured":"Wang, Z. et al. Fairness-aware adversarial perturbation towards bias mitigation for deployed deep models. In: Proc. IEEE\/CVF Conference on Computer Vision and Pattern Recognition. (CVPR 2022), 10379\u201310388 (2022).","DOI":"10.1109\/CVPR52688.2022.01013"},{"key":"1641_CR15","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice, L. R. Measures of the amount of ecologic association between species. Ecology 26, 297\u2013302 (1945).","journal-title":"Ecology"},{"key":"1641_CR16","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJ Van Griethuysen","year":"2017","unstructured":"Van Griethuysen, J. J. et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77, e104\u2013e107 (2017).","journal-title":"Cancer Res."},{"key":"1641_CR17","unstructured":"Tian, Y. et al. Fairseg: A large-scale medical image segmentation dataset for fairness learning using segment anything model with fair error-bound scaling. The Twelfth International Conference on Learning Representations (ICLR 2024)."},{"key":"1641_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z. et al. Towards fairness in visual recognition: effective strategies for bias mitigation. Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR 2020), 8919\u20138928 (2020).","DOI":"10.1109\/CVPR42600.2020.00894"},{"key":"1641_CR19","unstructured":"Manning, C. & Schutze, H. Foundations of statistical natural language processing (MIT Press, 1999)."},{"key":"1641_CR20","unstructured":"Van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)."},{"key":"1641_CR21","doi-asserted-by":"crossref","unstructured":"Davies, D. L. & Bouldin, D. W. A cluster separation measure. IEEE Trans. Pattern Anal. Mach. Intell. 224\u2013227 (1979).","DOI":"10.1109\/TPAMI.1979.4766909"},{"key":"1641_CR22","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53\u201365 (1987).","journal-title":"J. Comput. Appl. Math."},{"key":"1641_CR23","first-page":"111318","volume":"37","author":"R Jin","year":"2024","unstructured":"Jin, R. et al. Fairmedfm: fairness benchmarking for medical imaging foundation models. Adv. Neural Inf. Process. Syst. 37, 111318\u2013111357 (2024).","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"1641_CR24","doi-asserted-by":"crossref","unstructured":"Kim, B., Kim, H., Kim, K., Kim, S. & Kim, J. Learning not to learn: training deep neural networks with biased data. In: Proc. of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2019), 9012\u20139020 (2019).","DOI":"10.1109\/CVPR.2019.00922"},{"key":"1641_CR25","doi-asserted-by":"publisher","unstructured":"shared datasets, S. A. Thyroid ultrasound cine-clip https:\/\/doi.org\/10.71718\/7m5n-rh16. Data set (2021).","DOI":"10.71718\/7m5n-rh16"},{"key":"1641_CR26","doi-asserted-by":"publisher","first-page":"2612","DOI":"10.1007\/s00330-017-5212-2","volume":"28","author":"C-p Ning","year":"2018","unstructured":"Ning, C.-p et al. Distribution patterns of microcalcifications in suspected thyroid carcinoma: a classification method helpful for diagnosis. Eur. Radiol. 28, 2612\u20132619 (2018).","journal-title":"Eur. Radiol."},{"key":"1641_CR27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. Med. Image Comput. Comput. Assist. Interv. MICCAI 2015: 18th Int. Conf., Munich, Germany, Oct. 5-9, 2015, Proc., Part III, 234\u2013241 (Springer, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1641_CR28","doi-asserted-by":"publisher","first-page":"103280","DOI":"10.1016\/j.media.2024.103280","volume":"97","author":"J Chen","year":"2024","unstructured":"Chen, J. et al. Transunet: rethinking the U-Net architecture design for medical image segmentation through the lens of transformers. Med. Image Anal. 97, 103280 (2024).","journal-title":"Med. Image Anal."},{"key":"1641_CR29","doi-asserted-by":"crossref","unstructured":"Tang, F., Wang, L., Ning, C., Xian, M. & Ding, J. Cmu-net: a strong convmixer-based medical ultrasound image segmentation network. 2023 IEEE 20th Int. Symp. Biomed. Imaging (ISBI 2023), 1\u20135 (IEEE, 2023).","DOI":"10.1109\/ISBI53787.2023.10230609"},{"key":"1641_CR30","doi-asserted-by":"crossref","unstructured":"Kirillov, A. et al. Segment anything. In Proc. 2023 IEEE\/CVF International Conference on Computer Vision, 4015\u20134026 (2023).","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"1641_CR31","doi-asserted-by":"publisher","first-page":"654","DOI":"10.1038\/s41467-024-44824-z","volume":"15","author":"J Ma","year":"2024","unstructured":"Ma, J. et al. Segment anything in medical images. Nat. Comm. 15, 654 (2024).","journal-title":"Nat. Comm."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01641-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01641-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01641-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,17]],"date-time":"2025-05-17T11:15:25Z","timestamp":1747480525000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-025-01641-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,17]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1641"],"URL":"https:\/\/doi.org\/10.1038\/s41746-025-01641-y","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,17]]},"assertion":[{"value":"24 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"291"}}