{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:52:58Z","timestamp":1784040778130,"version":"3.55.0"},"reference-count":56,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,9,1]],"date-time":"2020-09-01T00:00:00Z","timestamp":1598918400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002920","name":"Hong Kong Research Grants Council","doi-asserted-by":"publisher","award":["14225616"],"award-info":[{"award-number":["14225616"]}],"id":[{"id":"10.13039\/501100002920","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1813204"],"award-info":[{"award-number":["U1813204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2020,9]]},"DOI":"10.1109\/tmi.2020.2974574","type":"journal-article","created":{"date-parts":[[2020,2,17]],"date-time":"2020-02-17T20:28:41Z","timestamp":1581971321000},"page":"2713-2724","source":"Crossref","is-referenced-by-count":298,"title":["MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data"],"prefix":"10.1109","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3921-5960","authenticated-orcid":false,"given":"Quande","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3416-9950","authenticated-orcid":false,"given":"Qi","family":"Dou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9315-6527","authenticated-orcid":false,"given":"Lequan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3055-5034","authenticated-orcid":false,"given":"Pheng Ann","family":"Heng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2935018"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2789181"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2017.7965852"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.4.041307"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.4.4.041302"},{"key":"ref37","article-title":"Semantic-guided encoder feature learning for blurry boundary delineation","author":"nie","year":"2019"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2928056"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2913184"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759554"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.2.021208"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-0600-0"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13416"},{"key":"ref2","first-page":"66","article-title":"Volumetric convnets with mixed residual connections for automated prostate segmentation from 3D MR images","author":"yu","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_54"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00753"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2963882"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1118\/1.2842076"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00847"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12048"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2201498"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2011.6091258"},{"key":"ref51","article-title":"Machine learning with multi-site imaging data: An empirical study on the impact of scanner effects","author":"glocker","year":"2019","journal-title":"Proc Med Imag Meets NeurIPS Workshop Adv Neural Inf Process Syst"},{"key":"ref56","first-page":"6447","article-title":"Domain generalization via model-agnostic learning of semantic features","author":"dou","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.12.002"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00919-9_17"},{"key":"ref53","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref11","article-title":"A strong baseline for domain adaptation and generalization in medical imaging","author":"yao","year":"2019","journal-title":"Proc Med Imag Deep Learn (MIDL)"},{"key":"ref40","first-page":"1","article-title":"Entropy-SGD: Biasing gradient descent into wide valleys","author":"chaudhari","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref12","first-page":"101","article-title":"Normalization of joint image-intensity statistics in MRI using the kullback-leibler divergence","author":"weisenfeld","year":"2005","journal-title":"Proc 2nd IEEE Int Symp Biomed Imag Macro Nano"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2014.08.008"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1117\/12.2293992"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2015.06.010"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24553-9_16"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.nicl.2018.04.037"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2014.2366792"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2019.07.006"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00746"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_13"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1117\/12.467167"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_58"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759295"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2013.00599"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/96"},{"key":"ref9","article-title":"CNN-based prostate zonal segmentation on T2-weighted MR images: A cross-dataset study","author":"rundo","year":"2019","journal-title":"arXiv 1903 12571"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2015.02.009"},{"key":"ref45","article-title":"Nci-Proc. IEEE-ISBI conf. 2013 Challenge: Automated segmentation of prostate structures","author":"bloch","year":"2015","journal-title":"The Cancer Imaging Archive"},{"key":"ref48","article-title":"Learning multiple visual domains with residual adapters","author":"rebuff","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00066"},{"key":"ref42","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2014","journal-title":"Proc Deep Learn Workshop Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_27"},{"key":"ref44","article-title":"Regularizing neural networks by penalizing confident output distributions","author":"pereyra","year":"2017","journal-title":"Proc Workshop Int Conf Learn Represent"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00454"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/9181677\/09000851.pdf?arnumber=9000851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:59:14Z","timestamp":1651067954000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9000851\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9]]},"references-count":56,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2020.2974574","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9]]}}}