{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:20:28Z","timestamp":1771698028991,"version":"3.50.1"},"reference-count":50,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"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":["61972157"],"award-info":[{"award-number":["61972157"]}],"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":["72192821"],"award-info":[{"award-number":["72192821"]}],"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":["62102151"],"award-info":[{"award-number":["62102151"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Sailing Program","award":["21YF1411200"],"award-info":[{"award-number":["21YF1411200"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0102"],"award-info":[{"award-number":["2021SHZDZX0102"]}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["22511104600"],"award-info":[{"award-number":["22511104600"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003399","name":"Shanghai Science and Technology Commission","doi-asserted-by":"publisher","award":["21511101200"],"award-info":[{"award-number":["21511101200"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAAI-Huawei MindSpore Open Fund","award":["CAAIXSJLJJ-2021-031A"],"award-info":[{"award-number":["CAAIXSJLJJ-2021-031A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1109\/tmi.2023.3264433","type":"journal-article","created":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:47:22Z","timestamp":1680716842000},"page":"2740-2750","source":"Crossref","is-referenced-by-count":50,"title":["MISSU: 3D Medical Image Segmentation via Self-Distilling TransUNet"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4062-4630","authenticated-orcid":false,"given":"Nan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0284-9940","authenticated-orcid":false,"given":"Shaohui","family":"Lin","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai, China"}]},{"given":"Xiaoxiao","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada"}]},{"given":"Ke","family":"Li","sequence":"additional","affiliation":[{"name":"Youtu Laboratory, Tencent, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3970-7519","authenticated-orcid":false,"given":"Yunhang","family":"Shen","sequence":"additional","affiliation":[{"name":"Youtu Laboratory, Tencent, Shanghai, China"}]},{"given":"Yue","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Software, Tsinghua University, Beijing, China"}]},{"given":"Lizhuang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2983721"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2012.231"},{"key":"ref15","first-page":"731","article-title":"An attention-oriented U-Net model and global feature for medical image segmentation","volume":"23","author":"han","year":"2020","journal-title":"J Appl Sci Eng"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2835303"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2878669"},{"key":"ref17","first-page":"384","article-title":"Models genesis: Generic autodidactic models for 3D medical image analysis","author":"zhou","year":"2019","journal-title":"Proc 22nd Int Conf Med Image Comput Comput Assist Intervent (MICCAI)"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.012"},{"key":"ref19","first-page":"661","article-title":"Multimodal self-supervised learning for medical image analysis","author":"taleb","year":"2021","journal-title":"Proc IPMI"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_75"},{"key":"ref45","first-page":"424","article-title":"3D U-Net: Learning dense volumetric segmentation from sparse annotation","author":"\u00e7i\u00e7ek","year":"2016","journal-title":"Proc 19th Int Conf Med Image Comput Comput -Assist Intervent (MICCAI)"},{"key":"ref48","article-title":"Medical transformer: Universal brain encoder for 3D MRI analysis","author":"jun","year":"2021","journal-title":"arXiv 2104 13633"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_49"},{"key":"ref42","article-title":"Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge","author":"bakas","year":"2018","journal-title":"arXiv 1811 02629"},{"key":"ref41","article-title":"Adam: Amethod for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref44","first-page":"534","article-title":"Medical image computing and computer assisted intervention&#x2014;MICCAI 2018","volume":"11073","author":"frangi","year":"2018","journal-title":"Proc 21st Int Conf"},{"key":"ref43","first-page":"1","article-title":"CHAOS-combined (CT-MR) healthy abdominal organ segmentation challenge data","author":"kavur","year":"2019","journal-title":"Proc ISBI"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.2986926"},{"key":"ref8","article-title":"TransClaw U-Net: Claw U-Net with transformers for medical image segmentation","author":"chang","year":"2021","journal-title":"arXiv 2107 05188"},{"key":"ref7","first-page":"1","article-title":"An image is worth 16 &#x00D7; 16 words: Transformers for image recognition at scale","author":"dosovitskiy","year":"2020","journal-title":"arXiv 2010 11929"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00275"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00937-3_60"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/3DV.2016.79"},{"key":"ref6","first-page":"109","article-title":"TransBTS: Multimodal brain tumor segmentation using transformer","author":"wang","year":"2021","journal-title":"Medical Image Computing and Computer Assisted Intervention&#x2014;MICCAI"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"ref40","first-page":"1","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"komodakis","year":"2017","journal-title":"Proc ICLR"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00110"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015628"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00060"},{"key":"ref36","article-title":"KD-ResUNet++: Automatic polyp segmentation via self-knowledge distillation","author":"kang","year":"2020","journal-title":"Proc Mediaeval Workshop"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3098703"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759262"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00938"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2021.3127688"},{"key":"ref2","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Med Image Comput Comput -Assisted Intervent"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.nima.2006.08.134"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref38","article-title":"Rethinking atrous convolution for semantic image segmentation","author":"chen","year":"2017","journal-title":"arXiv 1706 05587"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3147426"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-1772-0"},{"key":"ref26","article-title":"TransUNet: Transformers make strong encoders for medical image segmentation","author":"chen","year":"2021","journal-title":"arXiv 2102 04306"},{"key":"ref25","article-title":"UNETR: Transformers for 3D medical image segmentation","author":"hatamizadeh","year":"2021","journal-title":"arXiv 2103 10504"},{"key":"ref20","first-page":"238","article-title":"Revisiting Rubik&#x2019;s cube: Self-supervised learning with volume-wise transformation for 3D medical image segmentation","author":"tao","year":"2020","journal-title":"Proc 23rd Int Conf Med Image Comput Comput Assist Intervent (MICCAI)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101539"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3075244"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00444"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.4018\/IJCVIP.2020100103"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32239-7_30"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10236920\/10092809.pdf?arnumber=10092809","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:21:47Z","timestamp":1695666107000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10092809\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":50,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2023.3264433","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}