{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T18:46:18Z","timestamp":1776278778239,"version":"3.50.1"},"reference-count":65,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC0118300"],"award-info":[{"award-number":["2019YFC0118300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M690609"],"award-info":[{"award-number":["2021M690609"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Jiangsu Natural Science Foundation","doi-asserted-by":"publisher","award":["BK20210224"],"award-info":[{"award-number":["BK20210224"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1109\/tmi.2021.3117888","type":"journal-article","created":{"date-parts":[[2021,10,5]],"date-time":"2021-10-05T05:02:17Z","timestamp":1633410137000},"page":"608-620","source":"Crossref","is-referenced-by-count":200,"title":["Inconsistency-Aware Uncertainty Estimation for Semi-Supervised Medical Image Segmentation"],"prefix":"10.1109","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4534-7318","authenticated-orcid":false,"given":"Yinghuan","family":"Shi","sequence":"first","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"given":"Tong","family":"Ling","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6121-5529","authenticated-orcid":false,"given":"Jiwen","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Automation, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2195-2847","authenticated-orcid":false,"given":"Yefeng","family":"Zheng","sequence":"additional","affiliation":[{"name":"Tencent Jarvis Lab, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9981-3738","authenticated-orcid":false,"given":"Qian","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Data and Computer Science, Shandong Women’s University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7091-0702","authenticated-orcid":false,"given":"Lei","family":"Qi","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2488-1813","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2007.12.003"},{"key":"ref2","first-page":"1","article-title":"Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks","volume-title":"Proc. Med. Imag. With Deep Learn.","author":"Ayhan"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_67"},{"key":"ref5","article-title":"MixMatch: A holistic approach to semi-supervised learning","author":"Berthelot","year":"2019","journal-title":"arXiv:1905.02249"},{"key":"ref6","first-page":"1613","article-title":"Weight uncertainty in neural network","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Blundell"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2017.04.041"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2835303"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32248-9_51"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01190"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759555"},{"key":"ref12","article-title":"Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks","author":"Christ","year":"2017","journal-title":"arXiv:1702.05970"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00380"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00020"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_78"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_52"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903562"},{"key":"ref21","first-page":"1861","article-title":"Probabilistic backpropagation for scalable learning of Bayesian neural networks","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","volume":"37","author":"Hern\u00e1ndez-Lobato"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00129"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.59275\/j.melba.2021-g79f"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_6"},{"key":"ref25","article-title":"Universal semi-supervised semantic segmentation","author":"Kalluri","year":"2018","journal-title":"arXiv:1811.10323"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2930068"},{"key":"ref27","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"Kendall","year":"2017","journal-title":"arXiv:1703.04977"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_63"},{"key":"ref29","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Laine"},{"key":"ref30","first-page":"6405","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lakshminarayanan"},{"key":"ref31","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)","author":"Lee"},{"key":"ref32","article-title":"Pancreas segmentation with probabilistic map guided bi-directional recurrent UNet","author":"Li","year":"2019","journal-title":"arXiv:1903.00923"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_54"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995319"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref36","first-page":"1708","article-title":"Structured and efficient variational deep learning with matrix Gaussian posteriors","volume-title":"Proc. 33rd Int. Conf. Mach. Learn.","author":"Louizos"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2911588"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.2999854"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107269"},{"key":"ref44","first-page":"601","article-title":"Mutual information deep regularization for semi-supervised segmentation","volume-title":"Medical Imaging With Deep Learning","author":"Peng","year":"2020"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref46","volume-title":"Data From Pancreas-CT","author":"Roth","year":"2016"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_79"},{"key":"ref49","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","author":"Sohn","year":"2020","journal-title":"arXiv:2001.07685"},{"key":"ref50","article-title":"A teacher-student framework for semi-supervised medical image segmentation from mixed supervision","author":"Sun","year":"2020","journal-title":"arXiv:2010.12219"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59725-2_45"},{"key":"ref52","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":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00262"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.01.103"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.014"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_53"},{"key":"ref57","article-title":"Non-local U-Net for biomedical image segmentation","author":"Wang","year":"2018","journal-title":"arXiv:1812.04103"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00931-1_70"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101766"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107756"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2905537"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66179-7_47"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_51"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2959609"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/9724621\/09558816.pdf?arnumber=9558816","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T22:58:46Z","timestamp":1705013926000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9558816\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":65,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2021.3117888","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3]]}}}