{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:11:38Z","timestamp":1783437098791,"version":"3.54.6"},"reference-count":43,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"8","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"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":["81771921"],"award-info":[{"award-number":["81771921"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901084"],"award-info":[{"award-number":["61901084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Biomed. Health Inform."],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1109\/jbhi.2022.3172978","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T19:30:51Z","timestamp":1651865451000},"page":"3673-3684","source":"Crossref","is-referenced-by-count":15,"title":["Learning COVID-19 Pneumonia Lesion Segmentation From Imperfect Annotations via Divergence-Aware Selective Training"],"prefix":"10.1109","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7795-8589","authenticated-orcid":false,"given":"Shuojue","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-158X","authenticated-orcid":false,"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Sun","sequence":"additional","affiliation":[{"name":"SenseTime Research, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangde","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Sun","sequence":"additional","affiliation":[{"name":"Qingdao Huangdao District Health Bureau, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8136-9816","authenticated-orcid":false,"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"West China Hospital-SenseTime Joint Lab, West China Biomedical Big Data Center, Sichuan University West China Hospital, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qijun","family":"Wang","sequence":"additional","affiliation":[{"name":"Qingdao Huangdao District People&#x2019;s Hospital, Qingdao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S1473-3099(20)30120-1"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.20202006.2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200034"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200075"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2020.2987975"},{"key":"ref6","article-title":"Lung infection quantification of COVID-19 in CT images with deep learning","author":"Shan","year":"2020"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101759"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1148\/ryct.2020200082"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14676"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref11","first-page":"8527","article-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels","volume-title":"Advances in Neural Information Processing Systems","volume":"31","author":"Han","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59725-2_56"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58558-7_46"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i6.16617"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.368"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3054746"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.104181"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2996645"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00250"},{"key":"ref20","article-title":"A weakly supervised region-based active learning method for COVID-19 segmentation in CT images","author":"Laradji","year":"2020"},{"key":"ref21","article-title":"GASNet: Weakly-supervised framework for COVID-19 lesion segmentation","author":"Xu","year":"2020"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11726-9_28"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"ref26","first-page":"2304","article-title":"MentorNet: Learning data-driven curriculum for very deep neural networks on corrupted labels","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"80","author":"Jiang","year":"2018"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3000314"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295309"},{"key":"ref30","first-page":"6912","article-title":"Curriculum labeling: Self-paced pseudo-labeling for semi-supervised learning","volume-title":"Proc. AAAI Conf. Artif. Intell.","volume":"8","author":"Cascante-Bonilla","year":"2021"},{"key":"ref31","first-page":"12236","article-title":"Few-cost salient object detection with adversarial-paced learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhang","year":"2020"},{"key":"ref32","article-title":"Self-paced learning for latent variable models","volume-title":"Advances in Neural Information Processing Systems","volume":"23","author":"Kumar","year":"2010"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-018-1112-4"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2881114"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00718"},{"key":"ref38","first-page":"1","article-title":"Mixmatch: A holistic approach to semi-supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Berthelot","year":"2019"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10894"},{"issue":"2","key":"ref40","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.","volume":"3","author":"Lee","year":"2013"},{"key":"ref41","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"119","author":"Chen","year":"2020"},{"key":"ref42","first-page":"1","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","year":"2017"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_30"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221020\/9855434\/09770406.pdf?arnumber=9770406","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T23:11:08Z","timestamp":1705965068000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9770406\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8]]},"references-count":43,"journal-issue":{"issue":"8"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2022.3172978","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8]]}}}