{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T07:57:02Z","timestamp":1790582222579,"version":"4.1.0"},"reference-count":34,"publisher":"IOP Publishing","issue":"5","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Biomed. Phys. Eng. Express"],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Semi-supervised medical image segmentation methods have drawn wide attention as they reduce reliance on heavily annotated data. However, existing models suffer from confirmation bias with limited annotations, and structural or parameter coupling hinders self-correction, especially for medical images with ambiguous boundaries, low contrast and complex backgrounds. To address these issues, we propose an uncertainty-guided decoupling and complementary network (UGDC-Net) for binary medical image segmentation. It uses a contrastive mechanism to filter high-uncertainty regions of sub-networks for avoiding model collapse, and a dynamic competition mechanism to tackle the weight coupling bottleneck in teacher-student frameworks. Reliable pseudo-labels from high-performance sub-networks and supplementary ones from inconsistent high-uncertainty regions are integrated to provide diverse supervision, enhancing the model\u2019s exploration of small targets. Experiments on left atrium, Pancreas-CT and ISIC datasets validate that UGDC-Net outperforms state-of-the-art methods on binary segmentation tasks.<\/jats:p>","DOI":"10.1088\/2057-1976\/aea90e","type":"journal-article","created":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T07:28:08Z","timestamp":1790580488000},"page":"055030","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertainty-guided decoupled complementary network for binary semi-supervised medical image segmentation"],"prefix":"10.1088","volume":"12","author":[{"given":"Dongsheng","family":"Wang","sequence":"first","affiliation":[{"name":"Hebei University of Engineering"},{"name":"Hebei University of Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9379-1906","authenticated-orcid":true,"given":"Xiaohan","family":"Lang","sequence":"additional","affiliation":[{"name":"Hebei University of Engineering"},{"name":"Hebei University of Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,28]]},"reference":[{"key":"bpexaea90ebib1","first-page":"pp 11514","type":"conference-proceedings","article-title":"Bidirectional copy-paste for semi-supervised medical image segmentation","author":"Bai","year":"2023"},{"key":"bpexaea90ebib2","first-page":"pp 205","type":"conference-proceedings","article-title":"SWIN-UNET: unet-like pure transformer for medical image segmentation","author":"Cao","year":"2022"},{"key":"bpexaea90ebib3","first-page":"pp 6912","type":"conference-proceedings","article-title":"Curriculum labeling: revisiting pseudo-labeling for semi-supervised learning","author":"Cascante-Bonilla","year":"2021"},{"key":"bpexaea90ebib4","first-page":"pp 2613","type":"conference-proceedings","article-title":"Semi-supervised semantic segmentation with cross pseudo supervision","author":"Chen","year":"2021"},{"key":"bpexaea90ebib5","first-page":"pp 168","type":"conference-proceedings","article-title":"Skin lesion analysis toward melanoma detection: a challenge at the 2017 international symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)","author":"Codella","year":"2018"},{"key":"bpexaea90ebib6","article-title":"Dtbs: dual-teacher bi-directional self-training for domain adaptation in nighttime semantic segmentation","author":"Huang","year":"2024","type":"conference-proceedings"},{"key":"bpexaea90ebib7","article-title":"Complementary consistency semi-supervised learning for 3D left atrial image segmentation","author":"Huang","year":"2022","type":"preprint"},{"key":"bpexaea90ebib8","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1109\/TMI.2022.3230943","type":"journal-article","article-title":"Missformer: an effective transformer for 2D medical image segmentation","volume":"42","author":"Huang","year":"2023","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-04-17","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-17","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-28","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}