{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:46:11Z","timestamp":1776887171549,"version":"3.51.2"},"reference-count":63,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tim.2022.3221131","type":"journal-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T20:36:34Z","timestamp":1668026194000},"page":"1-1","source":"Crossref","is-referenced-by-count":16,"title":["Disentangled Representation for Cross-Domain Medical Image Segmentation"],"prefix":"10.1109","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2567-8761","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"first","affiliation":[{"name":"Fujitsu R&D Center, Co., LTD, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1697-6932","authenticated-orcid":false,"given":"Chaoliang","family":"Zhong","sequence":"additional","affiliation":[{"name":"Fujitsu R&D Center, Co., LTD, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3861-0042","authenticated-orcid":false,"given":"Cheng","family":"Feng","sequence":"additional","affiliation":[{"name":"Fujitsu R&D Center, Co., LTD, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6020-3633","authenticated-orcid":false,"given":"Ying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fujitsu R&D Center, Co., LTD, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0967-4859","authenticated-orcid":false,"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"Fujitsu R&D Center, Co., LTD, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0512-6318","authenticated-orcid":false,"given":"Yasuto","family":"Yokota","sequence":"additional","affiliation":[{"name":"Fujitsu LTD, Kawasaki, Japan"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500547"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2019.8793510"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3017037"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3047430"},{"key":"ref6","first-page":"97","article-title":"Learning transferable features with deep adaptation networks","volume-title":"Proc. ICML","author":"Long"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref10","first-page":"1645","article-title":"Conditional adversarial domain adaptation","volume-title":"Proc. NeurIPS","author":"Long"},{"key":"ref11","first-page":"5423","article-title":"Learning semantic representations for unsupervised domain adaptation","volume-title":"Proc. ICML","author":"Xie"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00780"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00189"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363637"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301865"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3122172"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2018.2828137"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/RBME.2010.2084567"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3071223"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1002\/rcs.1848"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2229278"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.3009073"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9341556"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/s21155163"},{"key":"ref26","article-title":"The little W-Net that could: State-of-the-art retinal vessel segmentation with minimalistic models","author":"Galdran","year":"2020","journal-title":"arXiv:2009.01907"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-009-5152-4"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2854886"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00875"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3009341"},{"issue":"1","key":"ref31","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11767"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2020.3031198"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_18"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5757"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.220"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506124"},{"key":"ref38","article-title":"Unsupervised domain adaptation for retinal vessel segmentation with adversarial learning and transfer normalization","author":"Feng","year":"2021","journal-title":"arXiv:2108.01821"},{"key":"ref39","article-title":"Isolating sources of disentanglement in variational","author":"Chen","year":"2018","journal-title":"arXiv:1802.04942"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240524"},{"key":"ref41","article-title":"Explicit disentanglement of appearance and perspective in generative models","author":"Detlefsen","year":"2019","journal-title":"arXiv:1906.11881"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01173"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00801"},{"issue":"7","key":"ref44","first-page":"38","article-title":"Distilling the knowledge in a neural network","volume":"14","author":"Hinton","year":"2015","journal-title":"Comput. Sci."},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00409"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00433"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01223"},{"key":"ref48","first-page":"1","article-title":"Self-ensembling for visual domain adaptation","volume-title":"Proc. ICLR","author":"French"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.01004"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2012.2205687"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2004.825627"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1155\/2013\/154860"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-20801-5_35"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2587062"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/42.845178"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3027665"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.2307\/j.ctvcm4g18.8"},{"key":"ref60","first-page":"1","article-title":"Adam: A method for stochastic optimization","volume-title":"Proc. ICLR","author":"Kingma"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.03.005"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3321408.3322627"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2974574"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/4407674\/09944699.pdf?arnumber=9944699","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T02:10:42Z","timestamp":1706753442000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9944699\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":63,"URL":"https:\/\/doi.org\/10.1109\/tim.2022.3221131","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}