{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T13:51:23Z","timestamp":1769262683545,"version":"3.49.0"},"reference-count":76,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"General Program of the National Natural Science Foundation of China","award":["62276189"],"award-info":[{"award-number":["62276189"]}]},{"name":"Tongji University Medicine-X Interdisciplinary Research Initiative","award":["2025-0650-YB-17"],"award-info":[{"award-number":["2025-0650-YB-17"]}]},{"name":"Tongji University Medicine-X Interdisciplinary Research Initiative","award":["2025-0650-YB-09"],"award-info":[{"award-number":["2025-0650-YB-09"]}]},{"name":"National Key R&D Program of China","award":["2024YFC3507100"],"award-info":[{"award-number":["2024YFC3507100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82472623"],"award-info":[{"award-number":["82472623"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Oriental Scholar Top Talent Program"},{"name":"High-Quality Development Project of Shanghai Economic and Information Commission","award":["2023-GZL-RGZN-01012"],"award-info":[{"award-number":["2023-GZL-RGZN-01012"]}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["2023ZY028"],"award-info":[{"award-number":["2023ZY028"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Emerg. Top. Comput. Intell."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1109\/tetci.2025.3593856","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T18:41:57Z","timestamp":1754678517000},"page":"607-620","source":"Crossref","is-referenced-by-count":0,"title":["Unifying Spatial and Semantic Augmentation Strategies for Cross-Domain Semi-Supervised Medical Image Segmentation"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2057-7512","authenticated-orcid":false,"given":"Huilin","family":"Lai","sequence":"first","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7052-7268","authenticated-orcid":false,"given":"Ye","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9071-9443","authenticated-orcid":false,"given":"Jianwei","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3309626"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102444"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107840"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2022.09.031"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-33718-5_12"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3178128"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180513"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"},{"issue":"2","key":"ref12","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop Challenges Representation Learn.","volume":"3","author":"Lee","year":"2013"},{"key":"ref13","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen","year":"2017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59713-9_46"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3015224"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3251380"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2023.3306253"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109347"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_42"},{"key":"ref21","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sohn","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01821-x"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412735"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-99-8132-8_33"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3354420"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58523-5_28"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01913-8"},{"key":"ref28","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhang","year":"2018"},{"key":"ref29","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma","year":"2019"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2973595"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109474"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10446303"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19803-8_25"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00566"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_26"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00858"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00200"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i2.20114"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3139999"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.111216"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_45"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3216293"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109881"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.110020"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_40"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3225687"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_47"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3648680"},{"key":"ref56","article-title":"Semi-supervised classification with graph convolutional networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kipf","year":"2016"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.12.017"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108987"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/ICME55011.2023.00099"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110280"},{"key":"ref61","article-title":"Semi-supervised domain generalization with known and unknown classes","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Zhang","year":"2024"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI52829.2022.9761561"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20217"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_29"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72949-2_5"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3611861"},{"key":"ref67","article-title":"SSL-DG: Rethinking and fusing semi-supervised learning and domain generalization in medical image segmentation","author":"Ye","year":"2023"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00044"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.03.010"},{"issue":"86","key":"ref72","first-page":"2579","article-title":"Visualizing data using T-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01092"},{"key":"ref74","first-page":"26007","article-title":"Semi-supervised semantic segmentation with prototype-based consistency regularization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Xu","year":"2022"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108238"}],"container-title":["IEEE Transactions on Emerging Topics in Computational Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/7433297\/11361307\/11120599.pdf?arnumber=11120599","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T21:01:09Z","timestamp":1769202069000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11120599\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":76,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tetci.2025.3593856","relation":{},"ISSN":["2471-285X"],"issn-type":[{"value":"2471-285X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]}}}