{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T16:49:26Z","timestamp":1792514966677,"version":"4.5.0"},"reference-count":73,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":"crossref","award":["61971142"],"award-info":[{"award-number":["61971142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11871165"],"award-info":[{"award-number":["11871165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62111530195"],"award-info":[{"award-number":["62111530195"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tpami.2022.3215186","type":"journal-article","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T21:07:05Z","timestamp":1667509625000},"page":"1-17","source":"Crossref","is-referenced-by-count":36,"title":["Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7179-4766","authenticated-orcid":false,"given":"Fuping","family":"Wu","sequence":"first","affiliation":[{"name":"School of Data Science, and Department of Statistics, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4351-4979","authenticated-orcid":false,"given":"Xiahai","family":"Zhuang","sequence":"additional","affiliation":[{"name":"School of Data Science, Fudan University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2901712"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref5","article-title":"Semi-supervised learning literature survey","volume":"1530","author":"Zhu","journal-title":"Comput. Sci. TR"},{"key":"ref6","first-page":"3546","article-title":"Semi-supervised learning with ladder network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rasmus"},{"key":"ref7","first-page":"1195","article-title":"Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref9","first-page":"529","article-title":"Semi-supervised learning by entropy minimization","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Grandvalet"},{"key":"ref10","first-page":"1","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume-title":"Proc. Workshop : Challenges Representation Learn.","author":"Lee"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0706-y"},{"key":"ref12","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kingma"},{"key":"ref13","article-title":"Unsupervised and semi-supervised learning with categorical generative adversarial networks","volume-title":"Proc. 4th Int. Conf. Learn. Representations","author":"Springenberg"},{"key":"ref14","first-page":"912","article-title":"Semi-supervised learning using gaussian fields and harmonic functions","volume-title":"Proc. 20th Int. Conf. Mach. Learn.","author":"Zhu"},{"key":"ref15","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hamilton"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_9"},{"key":"ref17","first-page":"5534","article-title":"Improved semi-supervised learning with GANs using manifold invariances","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kumar"},{"key":"ref18","volume-title":"Foundations of Machine Learning","author":"Mohri","year":"2018"},{"key":"ref19","first-page":"2998","article-title":"Semi-supervised classification based on classification from positive and unlabeled data","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Sakai"},{"key":"ref20","first-page":"703","article-title":"Analysis of learning from positive and unlabeled data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Plessis"},{"key":"ref21","first-page":"1675","article-title":"Positive-unlabeled learning with non-negative risk estimator","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Kiryo"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.186"},{"issue":"1","key":"ref24","first-page":"2399","article-title":"Manifold regularization: A geometric framework for learning from labeled and unlabeled examples","volume":"7","author":"Belkin","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref25","article-title":"An overview of deep semi-supervised learning","author":"Ouali","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.007"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107269"},{"key":"ref28","first-page":"1","article-title":"Auto-encoding variational bayes","volume-title":"Proc. 2nd Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref32","first-page":"3365","article-title":"Learning with pseudo-ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Bachman"},{"key":"ref33","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Rasmus"},{"key":"ref34","first-page":"1163","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sajjadi"},{"key":"ref35","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. 5th Int. Conf. Learn. Representations","author":"Laine"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/278"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58526-6_9"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00126"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.606"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_47"},{"key":"ref44","first-page":"1","article-title":"Adversarial learning for semi-supervised semantic segmentation","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Hung"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.03.009"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_43"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101766"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref49","first-page":"1","article-title":"Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Li"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.59275\/j.melba.2021-g79f"},{"key":"ref51","first-page":"16686","article-title":"Self-paced contrastive learning for semi-supervised medical image segmentation with meta-labels","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Peng"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00020"},{"key":"ref53","first-page":"1","article-title":"Semi-supervised medical image segmentation via cross teaching between CNN and transformer","author":"Luo","year":"2022","journal-title":"Proc. Med. Imag. Deep Learn."},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_30"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102517"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102146"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401920"},{"key":"ref60","first-page":"1386","article-title":"Convex formulation for learning from positive and unlabeled data","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Plessis"},{"key":"ref61","first-page":"2685","article-title":"Estimating the class prior and posterior from noisy positives and unlabeled data","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Jain"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.5555\/1642194.1642224"},{"key":"ref64","volume-title":"Foundations of Machine Learning","author":"Mohri","year":"2012"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101570"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101537"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2009.2013851"},{"key":"ref69","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma"},{"key":"ref70","first-page":"1195","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.","author":"Tarvainen"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2020.101786"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09921323.pdf?arnumber=9921323","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T22:57:47Z","timestamp":1705964267000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9921323\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":73,"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3215186","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}