{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:38:20Z","timestamp":1780511900975,"version":"3.54.1"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"HKSAR Research Grants Council (RGC) General Research Fund","award":["16203319"],"award-info":[{"award-number":["16203319"]}]},{"name":"Foshan HKUST Projects","award":["FSUST21-HKUST10E"],"award-info":[{"award-number":["FSUST21-HKUST10E"]}]},{"name":"Foshan HKUST Projects","award":["FSUST21-HKUST11E"],"award-info":[{"award-number":["FSUST21-HKUST11E"]}]},{"name":"Shenzhen Municipal Central Government Guides Local Science and Technology Development Special Funded Projects","award":["2021Szvup139"],"award-info":[{"award-number":["2021Szvup139"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1109\/tnnls.2023.3296652","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T17:34:15Z","timestamp":1690565655000},"page":"16589-16601","source":"Crossref","is-referenced-by-count":22,"title":["Exploring Feature Representation Learning for Semi-Supervised Medical Image Segmentation"],"prefix":"10.1109","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9684-7617","authenticated-orcid":false,"given":"Huimin","family":"Wu","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-8083","authenticated-orcid":false,"given":"Xiaomeng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3885-4912","authenticated-orcid":false,"given":"Kwang-Ting","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering and the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2845918"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2878669"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_30"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995319"},{"key":"ref8","first-page":"529","article-title":"Semi-supervised learning by entropy minimization","volume-title":"Proc. 17th Int. Conf. Neural Inf. Process. Syst.","author":"Grandvalet"},{"key":"ref9","first-page":"1","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop Challenges Represent. Learn. (ICML)","volume":"3","author":"Lee"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_32"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00941"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3029161"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_53"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102447"},{"key":"ref16","first-page":"6965","article-title":"A probabilistic U-Net for segmentation of ambiguous images","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Kohl"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_14"},{"key":"ref18","first-page":"12756","article-title":"Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Monteiro"},{"key":"ref19","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2012.2186825"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3228380"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3212985"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3157688"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2023.3241211"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2494582"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900118"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3144036"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101766"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_55"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_52"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref34","article-title":"Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model","volume-title":"Proc. BMVC","author":"Li"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32226-7_90"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_54"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_63"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref39","first-page":"109","article-title":"Efficient inference in fully connected CRFs with Gaussian edge potentials","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"24","author":"Kr\u00e4henb\u00fchl"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_60"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/278"},{"key":"ref45","first-page":"6","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","volume":"4","author":"Samuli"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3161829"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102564"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_47"},{"key":"ref51","first-page":"12546","article-title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chaitanya"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-72084-1_10"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00126"},{"key":"ref54","article-title":"Contrastive learning for label-efficient semantic segmentation","author":"Zhao","year":"2020","journal-title":"arXiv:2012.06985"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/BF01469225"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2004.1315232"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.jspi.2013.03.018"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"ref60","article-title":"Semantic distribution-aware contrastive adaptation for semantic segmentation","author":"Li","year":"2021","journal-title":"arXiv:2105.05013"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"ref62","article-title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","author":"Simpson","year":"2019","journal-title":"arXiv:1902.09063"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/504"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16434-7_65"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00218"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16852"},{"key":"ref68","first-page":"8082","article-title":"Class-imbalanced semi-supervised learning with adaptive thresholding","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/10737991\/10197243.pdf?arnumber=10197243","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T19:27:34Z","timestamp":1732735654000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10197243\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":68,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2023.3296652","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11]]}}}