{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:25:37Z","timestamp":1773840337482,"version":"3.50.1"},"reference-count":21,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"Establishment and Demonstration of Smart City Platform to Support Untact Public Services Based on Artificial Intelligence (AI) Kiosk","award":["S3037924"],"award-info":[{"award-number":["S3037924"]}]},{"name":"\u201cHigh-performance computing (HPC) Support\u201d Project of the Ministry of Science and Information and Communications Technology"},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3222788","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T20:33:54Z","timestamp":1668717234000},"page":"132376-132383","source":"Crossref","is-referenced-by-count":6,"title":["Progressive Weighted Self-Training Ensemble for Multi-Type Skin Lesion Semantic Segmentation"],"prefix":"10.1109","volume":"10","author":[{"given":"Cheolwon","family":"Lee","sequence":"first","affiliation":[{"name":"AI Research and Development Center, Lulu Lab Inc., Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sangwook","family":"Yoo","sequence":"additional","affiliation":[{"name":"AI Research and Development Center, Lulu Lab Inc., Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Semin","family":"Kim","sequence":"additional","affiliation":[{"name":"AI Research and Development Center, Lulu Lab Inc., Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jongha","family":"Lee","sequence":"additional","affiliation":[{"name":"AI Research and Development Center, Lulu Lab Inc., Seoul, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"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\/CVPR.2017.660"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref5","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"arXiv:1610.02242","author":"Laine","year":"2016"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref7","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tan"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref11","article-title":"MixMatch: A holistic approach to semi-supervised learning","volume-title":"arXiv:1905.02249","author":"Berthelot","year":"2019"},{"key":"ref12","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Sohn"},{"key":"ref13","article-title":"ReMixMatch: Semi-supervised learning with distribution alignment and augmentation anchoring","volume-title":"arXiv:1911.09785","author":"Berthelot","year":"2019"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2018.8451750"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2017.2778881"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2994033"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2868171"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2003.1227801"},{"key":"ref19","article-title":"On denoising autoencoders trained to minimise binary cross-entropy","volume-title":"arXiv:1708.08487","author":"Creswell","year":"2017"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2006.880587"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.5040\/9781501317460.ch-004"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09953982.pdf?arnumber=9953982","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:46:13Z","timestamp":1706755573000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9953982\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":21,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3222788","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}