{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:20:30Z","timestamp":1772302830178,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20461"],"award-info":[{"award-number":["U23A20461"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,11]]},"DOI":"10.1007\/s11517-025-03391-1","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T06:30:59Z","timestamp":1749450659000},"page":"3251-3262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-task and multi-scale attention network for lymph node metastasis prediction in esophageal cancer"],"prefix":"10.1007","volume":"63","author":[{"given":"Yan","family":"Yi","sequence":"first","affiliation":[]},{"given":"Jiacheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhenjiang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Liansheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Xiuping","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Qichao","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Baosheng","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,9]]},"reference":[{"key":"3391_CR1","doi-asserted-by":"crossref","unstructured":"Enzinger PC, Mayer RJ (2003) Esophageal cancer. New England J Med 349(23):2241\u20132252","DOI":"10.1056\/NEJMra035010"},{"issue":"3","key":"3391_CR2","doi-asserted-by":"publisher","first-page":"161","DOI":"10.14423\/SMJ.0000000000001226","volume":"114","author":"M DiSiena","year":"2021","unstructured":"DiSiena M, Perelman A, Birk J, Rezaizadeh H (2021) Esophageal cancer: an updated review. South Med J 114(3):161\u2013168","journal-title":"South Med J"},{"issue":"2","key":"3391_CR3","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.rcl.2020.11.008","volume":"59","author":"SL Betancourt-Cuellar","year":"2021","unstructured":"Betancourt-Cuellar SL, Benveniste MF, Palacio DP, Hofstetter WL (2021) Esophageal cancer: tumor-node-metastasis staging. Radiol Clin 59(2):219\u2013229","journal-title":"Radiol Clin"},{"issue":"4","key":"3391_CR4","doi-asserted-by":"publisher","first-page":"006","DOI":"10.1093\/dote\/doad006","volume":"36","author":"S Matsuda","year":"2023","unstructured":"Matsuda S, Takeuchi M, Kawakubo H, Kitagawa Y (2023) Lymph node metastatic patterns and the development of multidisciplinary treatment for esophageal cancer. Dis Esophagus 36(4):006","journal-title":"Dis Esophagus"},{"issue":"1","key":"3391_CR5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.gie.2018.07.037","volume":"89","author":"Y Horie","year":"2019","unstructured":"Horie Y, Yoshio T, Aoyama K, Yoshimizu S, Horiuchi Y, Ishiyama A, Hirasawa T, Tsuchida T, Ozawa T, Ishihara S et al (2019) Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 89(1):25\u201332","journal-title":"Gastrointest Endosc"},{"key":"3391_CR6","doi-asserted-by":"publisher","first-page":"612","DOI":"10.1007\/s10388-021-00826-0","volume":"18","author":"M Takeuchi","year":"2021","unstructured":"Takeuchi M, Seto T, Hashimoto M, Ichihara N, Morimoto Y, Kawakubo H, Suzuki T, Jinzaki M, Kitagawa Y, Miyata H et al (2021) Performance of a deep learning-based identification system for esophageal cancer from CT images. Esophagus 18:612\u2013620","journal-title":"Esophagus"},{"key":"3391_CR7","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.ejmp.2021.02.026","volume":"83","author":"AM Barrag\u00e1n-Montero","year":"2021","unstructured":"Barrag\u00e1n-Montero AM, Thomas M, Defraene G, Michiels S, Haustermans K, Lee JA, Sterpin E (2021) Deep learning dose prediction for IMRT of esophageal cancer: the effect of data quality and quantity on model performance. Phys Med 83:52\u201363","journal-title":"Phys Med"},{"issue":"1","key":"3391_CR8","first-page":"4034404","volume":"2022","author":"J Wang","year":"2022","unstructured":"Wang J, Zeng J, Li H, Yu X (2022) A deep learning radiomics analysis for survival prediction in esophageal cancer. J Healthcare Eng 2022(1):4034404","journal-title":"J Healthcare Eng"},{"key":"3391_CR9","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang H, Xiao J, Nie L, Shao J, Liu W, Chua T-S (2017) SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5659\u20135667","DOI":"10.1109\/CVPR.2017.667"},{"key":"3391_CR10","doi-asserted-by":"crossref","unstructured":"Woo S, Park J, Lee J-Y, Kweon IS (2018) CBAM: convolutional block attention module. In: Proceedings of the european conference on computer vision (ECCV), pp 3\u201319","DOI":"10.1007\/978-3-030-01234-2_1"},{"issue":"5","key":"3391_CR11","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1007\/s11517-024-03018-x","volume":"62","author":"X Zhao","year":"2024","unstructured":"Zhao X, Lai L, Li Y, Zhou X, Cheng X, Chen Y, Huang H, Guo J, Wang G (2024) A lightweight bladder tumor segmentation method based on attention mechanism. Med Biol Eng Comput 62(5):1519\u20131534","journal-title":"Med Biol Eng Comput"},{"key":"3391_CR12","doi-asserted-by":"crossref","unstructured":"Liao L, Lu J, Wang L, Zhang Y, Gao D, Wang M (2024) CT-Net: an interpretable CNN-transformer fusion network for fNIRS classification. Med Biol Eng Comput 1\u201315","DOI":"10.1007\/s11517-024-03138-4"},{"issue":"1","key":"3391_CR13","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1093\/nsr\/nwx105","volume":"5","author":"Y Zhang","year":"2018","unstructured":"Zhang Y, Yang Q (2018) An overview of multi-task learning. Natl Sci Rev 5(1):30\u201343","journal-title":"Natl Sci Rev"},{"issue":"12","key":"3391_CR14","doi-asserted-by":"publisher","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","volume":"34","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Yang Q (2021) A survey on multi-task learning. IEEE Trans Knowl Data Eng 34(12):5586\u20135609","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"3391_CR15","doi-asserted-by":"crossref","unstructured":"Jian M, Wu R, Xu W, Zhi H, Tao C, Chen H, Li X (2024) Vascuconnet: an enhanced connectivity network for vascular segmentation. Med Biol Eng Comput 1\u201312","DOI":"10.1007\/s11517-024-03150-8"},{"key":"3391_CR16","doi-asserted-by":"crossref","unstructured":"Qian X, Fu Y, Jiang Y-G, Xiang T, Xue X (2017) Multi-scale deep learning architectures for person re-identification. In: Proceedings of the IEEE international conference on computer vision, pp 5399\u20135408","DOI":"10.1109\/ICCV.2017.577"},{"key":"3391_CR17","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.inffus.2022.08.010","volume":"89","author":"JE Arco","year":"2023","unstructured":"Arco JE, Ortiz A, Ramirez J, Martinez-Murcia FJ, Zhang Y-D, Gorriz JM (2023) Uncertainty-driven ensembles of multi-scale deep architectures for image classification. Inf Fusion 89:53\u201365","journal-title":"Inf Fusion"},{"key":"3391_CR18","unstructured":"Xu Z-QJ, Yao J, Yi Y, Hang L, Zhang Y, Zhang T, et al (2024) Solving multiscale dynamical systems by deep learning. arXiv:2401.01220"},{"key":"3391_CR19","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3391_CR20","unstructured":"Tan M (2019) Efficientnet: rethinking model scaling for convolutional neural networks. arXiv:1905.11946"},{"key":"3391_CR21","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03391-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03391-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03391-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T07:30:50Z","timestamp":1763623850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03391-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,9]]},"references-count":21,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["3391"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03391-1","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,9]]},"assertion":[{"value":"1 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors Yan Yi, Zhenjiang Li, Xiuping Ding, and Baosheng Li work for Shandong First Medical University. Author Yong Huang works for Shandong Cancer Hospital. Authors Jiacheng Wang and QichaoZhou serve at Manteia Technology, Co., Ltd. Author Liansheng Wang is a professor at Xiamen University.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Informed consent was obtained from all subjects involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}