{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T20:01:26Z","timestamp":1781035286408,"version":"3.54.1"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"6","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"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":["62322604"],"award-info":[{"award-number":["62322604"]}],"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":["62576207"],"award-info":[{"award-number":["62576207"]}],"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":[[2026,6]]},"DOI":"10.1109\/jbhi.2025.3637281","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T19:04:44Z","timestamp":1764183884000},"page":"5167-5180","source":"Crossref","is-referenced-by-count":0,"title":["DMformer: Difficulty-Adapted Masked Transformer for Semi-Supervised Medical Image Segmentation"],"prefix":"10.1109","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-4066-7929","authenticated-orcid":false,"given":"Zelin","family":"Peng","sequence":"first","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9606-7052","authenticated-orcid":false,"given":"Guanchun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi&#x0027;an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-1801-0063","authenticated-orcid":false,"given":"Zhengqin","family":"Xu","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4029-3322","authenticated-orcid":false,"given":"Xiaokang","family":"Yang","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1235-598X","authenticated-orcid":false,"given":"Wei","family":"Shen","sequence":"additional","affiliation":[{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_54"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16452-1_14"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_39"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87193-2_29"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3213372"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref7","article-title":"How to understand masked autoencoders","author":"Cao","year":"2022"},{"key":"ref8","first-page":"2022","article-title":"iBOT: Image bert pre-training with online tokenizer","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhou"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01000"},{"key":"ref10","first-page":"1195","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":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2837502"},{"key":"ref13","article-title":"Miccai multi-atlas labeling beyond the cranial vaultworkshop and challenge","volume-title":"Proc. MICCAI Multi-Atlas Labeling Beyond Cranial VaultWorkshop Challenge","author":"Landman","year":"2015"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"ref15","first-page":"22106","article-title":"Semi-supervised semantic segmentation via adaptive equalization learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Hu","year":"2021"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00421"},{"key":"ref17","article-title":"Adversarial learning for semi-supervised semantic segmentation","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Hung","year":"2018"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3144036"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00685"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"ref21","first-page":"2021","article-title":"PseudoSeg: Designing pseudo labels for semantic segmentation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zou"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.5244\/C.34.154"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58601-0_26"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00126"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00812"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"ref28","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","year":"2020"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0203"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3117564"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66185-8_29"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2995319"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_63"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3228316"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16440-8_61"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00941"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3161829"},{"issue":"1","key":"ref41","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/201"},{"key":"ref43","first-page":"820","article-title":"Semi-supervised medical image segmentation via cross teaching between CNN and transformer","volume-title":"Proc. Int. Conf. Med. Imag. Deep Learn.","author":"Luo","year":"2022"},{"key":"ref44","first-page":"12546","article-title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Chaitanya","year":"2020"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87196-3_42"},{"key":"ref46","first-page":"2022","article-title":"Beit: Bert pre-training of image transformers","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Bao"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1039"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1137\/0330046"},{"key":"ref50","first-page":"2023","article-title":"Freematch: Self-adaptive thresholding for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Wang"},{"key":"ref51","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo","year":"2017"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1119\/1.15378"},{"key":"ref53","first-page":"5824","article-title":"Gradient surgery for multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Yu","year":"2020"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"ref55","first-page":"1581","article-title":"Inherent consistent learning for accurate semi-supervised medical image segmentation","volume-title":"Proc. Med. Imag. Deep Learn.","author":"Zhu","year":"2024"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.52202\/068431-2661"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1016\/S2589-7500(24)00154-7"},{"key":"ref59","article-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-59710-8_64"},{"key":"ref61","article-title":"U-mamba: Enhancing long-range dependency for biomedical image segmentation","author":"Ma","year":"2024"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00503"},{"key":"ref65","first-page":"18878","article-title":"Conflict-averse gradient descent for multi-task learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Liu","year":"2021"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3143554"}],"container-title":["IEEE Journal of Biomedical and Health Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6221020\/11552637\/11269316.pdf?arnumber=11269316","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T19:53:35Z","timestamp":1781034815000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11269316\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":66,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.1109\/jbhi.2025.3637281","relation":{},"ISSN":["2168-2194","2168-2208"],"issn-type":[{"value":"2168-2194","type":"print"},{"value":"2168-2208","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6]]}}}