{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T23:24:42Z","timestamp":1784935482618,"version":"3.55.0"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6230012077"],"award-info":[{"award-number":["6230012077"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Municipal Central Guided Local Science and Technology Development Fund Project","award":["YDZX20233100001001"],"award-info":[{"award-number":["YDZX20233100001001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1109\/tmi.2025.3594081","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T18:51:06Z","timestamp":1753901466000},"page":"177-189","source":"Crossref","is-referenced-by-count":10,"title":["Dual Cross-Image Semantic Consistency With Self-Aware Pseudo Labeling for Semi-Supervised Medical Image Segmentation"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6838-7911","authenticated-orcid":false,"given":"Han","family":"Wu","sequence":"first","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0022-0217","authenticated-orcid":false,"given":"Chong","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Stanford University, Stanford, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3798-4504","authenticated-orcid":false,"given":"Zhiming","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering and the State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2898414"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3225667"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2025.103551"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3566425"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3306781"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02001"},{"key":"ref7","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Laine"},{"key":"ref8","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Int. Conf. Adv. Neural Inf. Process. Syst.","volume":"30","author":"Tarvainen"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20351-1_43"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3161829"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102880"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3162043"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103111"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105665"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_32"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093608"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101766"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16437-8_1"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3233648"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3541830"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72086-4_15"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43895-0_18"},{"key":"ref28","first-page":"6256","article-title":"Unsupervised data augmentation for consistency training","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Xie"},{"key":"ref29","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sohn"},{"key":"ref30","first-page":"11525","article-title":"Dash: Semi-supervised learning with dynamic thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Xu"},{"key":"ref31","first-page":"1","article-title":"AdaMatch: A unified approach to semi-supervised learning and domain adaptation","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Berthelot"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00889-5_2"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106152"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00197"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102726"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00428"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref39","first-page":"1","article-title":"Mixup: Beyond empirical risk minimization","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhang"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101832"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02046"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_54"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43907-0_10"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3400840"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2024.3468896"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72652-1_14"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM62325.2024.10821816"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_30"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref57","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017","journal-title":"arXiv:1708.04552"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00294"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3273390"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105902"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105636"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11328978\/11104231.pdf?arnumber=11104231","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T18:35:56Z","timestamp":1767724556000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11104231\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":61,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3594081","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]}}}