{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T23:52:06Z","timestamp":1781653926665,"version":"3.54.5"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["W2411054, U21A20521 and 62271178"],"award-info":[{"award-number":["W2411054, U21A20521 and 62271178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jiangyin Municipal Health Commission Scientific Research Program","award":["M202402"],"award-info":[{"award-number":["M202402"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s11517-026-03577-1","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T03:46:34Z","timestamp":1777520794000},"page":"2251-2267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BAMT-Net: a boundary-aware multi-task framework for 3D breast ultrasound lesion segmentation and classification"],"prefix":"10.1007","volume":"64","author":[{"given":"Shuang","family":"He","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingwei","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juxiang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"issue":"6","key":"3577_CR1","doi-asserted-by":"publisher","first-page":"777","DOI":"10.3390\/life14060777","volume":"14","author":"L Katsika","year":"2024","unstructured":"Katsika L, Boureka E, Kalogiannidis I, Tsakiridis I, Tirodimos I, Lallas K, Tsimtsiou Z, Dagklis T (2024) Screening for breast cancer: a comparative review of guidelines. Life 14(6):777","journal-title":"Life"},{"key":"3577_CR2","first-page":"174550572413070","volume":"21","author":"KA Fairman","year":"2025","unstructured":"Fairman KA (2025) Patient perspective: Is intensive screening of women at high risk of breast cancer evidence-based medicine or d\u00e9j\u00e0 vu? Womens Health 21:17455057241307088","journal-title":"Womens Health"},{"issue":"10","key":"3577_CR3","doi-asserted-by":"publisher","first-page":"e230023","DOI":"10.1148\/rg.230023","volume":"43","author":"AI Huppe","year":"2023","unstructured":"Huppe AI, Inciardi MF, Aripoli AM, Peterson JK, Smith CB, Winblad OD (2023) Pearls and pitfalls of interpretation of automated breast us. Radiographics 43(10):e230023","journal-title":"Radiographics"},{"issue":"1","key":"3577_CR4","doi-asserted-by":"publisher","first-page":"33","DOI":"10.4274\/ejbh.galenos.2024.2024-9-7","volume":"21","author":"F \u00c7elebi","year":"2025","unstructured":"\u00c7elebi F, Tuncer O, Oral M, Duymaz T, Orhan T, Erta\u015f G (2025) Artificial intelligence in diagnostic breast ultrasound: a comparative analysis of decision support among radiologists with various levels of expertise. European J Breast Health 21(1):33","journal-title":"European J Breast Health"},{"key":"3577_CR5","doi-asserted-by":"publisher","unstructured":"Luo G, Xu M, Chen H, Liang X, Tao X, Ni D, Jeong H, Kim C, Stock R, Baumgartner M, Kirchhoff Y, Rokuss M, Maier-Hein K, Yang Z, Fan T, Boutry N, Tereshchenko D, Moine A, Charmetant M, Sauer J, Du H, Bai X-H, Raikar VP, Montoya-del-Angel R, Marti R, Luna M, Lee D, Qayyum A, Mazher M, Guo Q, Wang C, Awasthi N, Zhao Q, Wang W, Wang K, Wang Q, Dong S (2025) Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge. https:\/\/doi.org\/10.48550\/arXiv.2501.15588","DOI":"10.48550\/arXiv.2501.15588"},{"key":"3577_CR6","doi-asserted-by":"publisher","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, Glocker B, Rueckert D (2018) Attention U-Net: Learning Where to Look for the Pancreas. https:\/\/doi.org\/10.48550\/arXiv.1804.03999","DOI":"10.48550\/arXiv.1804.03999"},{"issue":"8","key":"3577_CR7","doi-asserted-by":"publisher","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","volume":"42","author":"J Hu","year":"2020","unstructured":"Hu J, Shen L, Albanie S, Sun G, Wu E (2020) Squeeze-and-Excitation Networks. IEEE Trans Pattern Anal Mach Intell 42(8):2011\u20132023. https:\/\/doi.org\/10.1109\/TPAMI.2019.2913372","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"3577_CR8","first-page":"121","volume":"3","author":"X Diao","year":"2013","unstructured":"Diao X, Chen Y, Pang Y (2013) Automated breast volume scanning: a case demonstration of a breast invasive ductal carcinoma. Quant Imaging Med Surg 3(2):121","journal-title":"Quant Imaging Med Surg"},{"issue":"1","key":"3577_CR9","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1177\/147323001204000130","volume":"40","author":"Q Zhang","year":"2012","unstructured":"Zhang Q, Hu B, Hu B, Li W (2012) Detection of breast lesions using an automated breast volume scanner system. J Int Med Res 40(1):300\u2013306","journal-title":"J Int Med Res"},{"issue":"11","key":"3577_CR10","doi-asserted-by":"publisher","first-page":"1457","DOI":"10.1016\/j.acra.2018.02.014","volume":"25","author":"R Rella","year":"2018","unstructured":"Rella R, Belli P, Giuliani M, Bufi E, Carlino G, Rinaldi P, Manfredi R (2018) Automated Breast Ultrasonography (ABUS) in the Screening and Diagnostic Setting: Indications and Practical Use. Acad Radiol 25(11):1457\u20131470. https:\/\/doi.org\/10.1016\/j.acra.2018.02.014","journal-title":"Acad Radiol"},{"issue":"2","key":"3577_CR11","doi-asserted-by":"publisher","first-page":"234","DOI":"10.2214\/AJR.13.12072","volume":"204","author":"RF Brem","year":"2015","unstructured":"Brem RF, Lenihan MJ, Lieberman J, Torrente J (2015) Screening breast ultrasound: Past, present, and future. AJR Am J Roentgenol 204(2):234\u2013240. https:\/\/doi.org\/10.2214\/AJR.13.12072","journal-title":"AJR Am J Roentgenol"},{"key":"3577_CR12","doi-asserted-by":"publisher","first-page":"106496","DOI":"10.1016\/j.compbiomed.2022.106496","volume":"153","author":"Y Zhao","year":"2023","unstructured":"Zhao Y, Wang X, Che T, Bao G, Li S (2023) Multi-task deep learning for medical image computing and analysis: A review. Comput Biol Med 153:106496. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106496","journal-title":"Comput Biol Med"},{"key":"3577_CR13","doi-asserted-by":"publisher","unstructured":"Wang R, Chen F, Chen H, Lin C, Shuai J, Wu Y, Ma L, Hu X, Wu M, Wang J, Zhao Q, Shuai J, Pan J Deep learning in digital breast tomosynthesis: current status, challenges, and future trends. https:\/\/doi.org\/10.1002\/mco2.70247","DOI":"10.1002\/mco2.70247"},{"issue":"5","key":"3577_CR14","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1111\/1754-9485.13713","volume":"68","author":"CJ Troupis","year":"2024","unstructured":"Troupis CJ, Knight RAH, Lau KK-P (2024) What is the appropriate measure of radiology workload: Study or image numbers? J Med Imaging Radiat Oncol 68(5):530\u2013539","journal-title":"J Med Imaging Radiat Oncol"},{"issue":"6","key":"3577_CR15","doi-asserted-by":"publisher","first-page":"2888","DOI":"10.21037\/qims-20-1409","volume":"11","author":"T Martin-Noguerol","year":"2021","unstructured":"Martin-Noguerol T, Luna A (2021) External validation of ai algorithms in breast radiology: the last healthcare security checkpoint? Quant Imaging Med Surg 11(6):2888","journal-title":"Quant Imaging Med Surg"},{"issue":"7","key":"3577_CR16","doi-asserted-by":"publisher","first-page":"1510","DOI":"10.1002\/lary.25882","volume":"126","author":"BJ Dixon","year":"2016","unstructured":"Dixon BJ, Chan H, Daly MJ, Qiu J, Vescan A, Witterick IJ, Irish JC (2016) Three-dimensional virtual navigation versus conventional image guidance: a randomized controlled trial. Laryngoscope 126(7):1510\u20131515","journal-title":"Laryngoscope"},{"key":"3577_CR17","doi-asserted-by":"publisher","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W (eds) Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016, Springer International Publishing, Cham, pp 424\u2013432. https:\/\/doi.org\/10.1007\/978-3-319-46723-8_49","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"3577_CR18","doi-asserted-by":"publisher","unstructured":"Tang X, Chen H, Ye H, Feng J (2023) SegResnet: COVID-19 Detection Method Based on Deep Learning. In: Shmaliy YS, Nayyar A (eds) 7th International Conference on Computing, Control and Industrial Engineering (CCIE 2023), Springer Nature, Singapore, pp 401\u2013406. https:\/\/doi.org\/10.1007\/978-981-99-2730-2_39","DOI":"10.1007\/978-981-99-2730-2_39"},{"issue":"2","key":"3577_CR19","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH (2021) nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203\u2013211. https:\/\/doi.org\/10.1038\/s41592-020-01008-z","journal-title":"Nat Methods"},{"key":"3577_CR20","doi-asserted-by":"publisher","unstructured":"Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth H, Xu D (2021) UNETR: Transformers for 3D medical image segmentation. https:\/\/doi.org\/10.48550\/arXiv.2103.10504","DOI":"10.48550\/arXiv.2103.10504"},{"key":"3577_CR21","doi-asserted-by":"publisher","unstructured":"Perera S, Navard P, Yilmaz A (2024) SegFormer3D: An efficient transformer for 3D medical image segmentation. https:\/\/doi.org\/10.48550\/arXiv.2404.10156","DOI":"10.48550\/arXiv.2404.10156"},{"key":"3577_CR22","unstructured":"Ma J, Yang Z, Kim S, Chen B, Baharoon M, Fallahpour A, Asakereh R, Lyu H, Wang B Medsam2: Segment anything in 3d medical images and videos, arXiv:2504.03600"},{"key":"3577_CR23","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y (2018) Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6546\u20136555","DOI":"10.1109\/CVPR.2018.00685"},{"key":"3577_CR24","doi-asserted-by":"publisher","unstructured":"Tan M, Le QV (2020) EfficientNet: Rethinking model scaling for convolutional neural networks. https:\/\/doi.org\/10.48550\/arXiv.1905.11946","DOI":"10.48550\/arXiv.1905.11946"},{"key":"3577_CR25","doi-asserted-by":"publisher","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE, Montreal, QC, Canada, pp 9992\u201310002. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00986","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"3577_CR26","doi-asserted-by":"publisher","unstructured":"Chowdary GJ, Yin Z (2024) Med-Former: A Transformer Based Architecture for Medical Image Classification. In: Linguraru MG, Dou Q, Feragen A, Giannarou S, Glocker B, Lekadir K, Schnabel JA (eds) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024, Springer Nature Switzerland, Cham, pp 448\u2013457. https:\/\/doi.org\/10.1007\/978-3-031-72120-5_42","DOI":"10.1007\/978-3-031-72120-5_42"},{"key":"3577_CR27","doi-asserted-by":"publisher","unstructured":"Dai Z, Liu H, Le QV, Tan M (2021) CoAtNet: Marrying convolution and attention for all data sizes. https:\/\/doi.org\/10.48550\/arXiv.2106.04803","DOI":"10.48550\/arXiv.2106.04803"},{"key":"3577_CR28","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. https:\/\/doi.org\/10.48550\/arXiv.2201.03545","DOI":"10.48550\/arXiv.2201.03545"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03577-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-026-03577-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-026-03577-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:56:42Z","timestamp":1781650602000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-026-03577-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":28,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["3577"],"URL":"https:\/\/doi.org\/10.1007\/s11517-026-03577-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,30]]},"assertion":[{"value":"23 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethics Committee of Jiangyin Traditional Chinese Medicine Hospital. The requirement for written informed consent was waived by the committee due to the retrospective nature of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving Human Participants"}},{"value":"In accordance with the relevant national ethical guidelines, the requirement for informed consent was waived by the committee.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}