{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T20:14:09Z","timestamp":1785183249126,"version":"3.55.0"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1016\/j.knosys.2026.115662","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T17:13:48Z","timestamp":1772730828000},"page":"115662","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["KP2L: Knowledge-driven pyramid prototype learning for semi-supervised medical image segmentation"],"prefix":"10.1016","volume":"340","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9677-3467","authenticated-orcid":false,"given":"Yuqi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3645-9046","authenticated-orcid":false,"given":"Yufei","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1934-1969","authenticated-orcid":false,"given":"Wei","family":"Fu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0536-1345","authenticated-orcid":false,"given":"Xiaodong","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0660-5436","authenticated-orcid":false,"given":"Thierry","family":"Den\u0153ux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2026.115662_bib0001","doi-asserted-by":"crossref","DOI":"10.1109\/TPAMI.2024.3435571","article-title":"Medical image segmentation review: the success of u-net","author":"Azad","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.knosys.2026.115662_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107840","article-title":"Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation","volume":"169","author":"Jiao","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.knosys.2026.115662_bib0003","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"8801","article-title":"Semi-supervised medical image segmentation through dual-task consistency","author":"Luo","year":"2021"},{"key":"10.1016\/j.knosys.2026.115662_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108506","article-title":"Triple-task mutual consistency for semi-supervised 3d medical image segmentation","volume":"175","author":"Chen","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"10.1016\/j.knosys.2026.115662_bib0005","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1109\/JBHI.2022.3162043","article-title":"All-around real label supervision: Cyclic prototype consistency learning for semi-supervised medical image segmentation","volume":"26","author":"Xu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.knosys.2026.115662_bib0006","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"662","article-title":"Upcol: uncertainty-informed prototype consistency learning for semi-supervised medical image segmentation","author":"Lu","year":"2023"},{"key":"10.1016\/j.knosys.2026.115662_bib0007","series-title":"RSS 2016 workshop: geometry and beyond-representations, physics, and scene understanding for robotics","article-title":"Signed distance fields: A natural representation for both mapping and planning","author":"Oleynikova","year":"2016"},{"key":"10.1016\/j.knosys.2026.115662_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102996","article-title":"On the challenges and perspectives of foundation models for medical image analysis","volume":"91","author":"Zhang","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.115662_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109818","article-title":"Target-aware u-net with fuzzy skip connections for refined pancreas segmentation","volume":"131","author":"Chen","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.knosys.2026.115662_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112203","article-title":"Semi-mamba-unet: Pixel-level contrastive and cross-supervised visual mamba-based unet for semi-supervised medical image segmentation","volume":"300","author":"Ma","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112835","article-title":"Mmseg: a novel multi-task learning framework for class imbalance and label scarcity in medical image segmentation","volume":"309","author":"Yang","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0012","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"4070","article-title":"Adaptive bidirectional displacement for semi-supervised medical image segmentation","author":"Chi","year":"2024"},{"key":"10.1016\/j.knosys.2026.115662_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112890","article-title":"Uncertainty-aware consistency learning for semi-supervised medical image segmentation","volume":"309","author":"Dong","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0014","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.neucom.2023.02.047","article-title":"Semi-supervised multiple evidence fusion for brain tumor segmentation","volume":"535","author":"Huang","year":"2023","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2026.115662_bib0015","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"305","article-title":"Frcnet: Frequency and region consistency for semi-supervised medical image segmentation","author":"He","year":"2024"},{"key":"10.1016\/j.knosys.2026.115662_bib0016","series-title":"International Conference on Learning Representations","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2017"},{"key":"10.1016\/j.knosys.2026.115662_bib0017","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume":"30","author":"Tarvainen","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0018","series-title":"Medical image computing and computer assisted intervention\u2013MICCAI 2019: 22nd international conference, Shenzhen, China, October 13\u201317, 2019, proceedings, part II 22","first-page":"605","article-title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation","author":"Yu","year":"2019"},{"key":"10.1016\/j.knosys.2026.115662_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102517","article-title":"Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency","volume":"80","author":"Luo","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.115662_bib0020","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"Snell","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0021","article-title":"Knowledge-driven prototype refinement for few-shot fine-grained recognition","author":"Chen","year":"2025","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0022","series-title":"BMVC","first-page":"4","article-title":"Few-shot semantic segmentation with prototype learning","volume":"3","author":"Dong","year":"2018"},{"key":"10.1016\/j.knosys.2026.115662_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111408","article-title":"A novel semi-supervised prototype network with two-stream wavelet scattering convolutional encoder for tbm main bearing few-shot fault diagnosis","volume":"286","author":"Fu","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0024","first-page":"1","article-title":"Mvpcl: multi-view prototype consistency learning for semi-supervised medical image segmentation","author":"Li","year":"2024","journal-title":"Vis. Comput."},{"key":"10.1016\/j.knosys.2026.115662_bib0025","first-page":"26007","article-title":"Semi-supervised semantic segmentation with prototype-based consistency regularization","volume":"35","author":"Xu","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.knosys.2026.115662_bib0026","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2","first-page":"908","article-title":"Evidential prototype learning for semi-supervised medical image segmentation","author":"He","year":"2025"},{"key":"10.1016\/j.knosys.2026.115662_bib0027","doi-asserted-by":"crossref","unstructured":"L. Li, Mixed prototype consistency learning for semi-supervised medical image segmentation, (2024) arXiv: 2404.10717.","DOI":"10.1109\/BIBM62325.2024.10821789"},{"key":"10.1016\/j.knosys.2026.115662_bib0028","series-title":"proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"2980","article-title":"Focal loss for dense object detection","author":"Ross","year":"2017"},{"key":"10.1016\/j.knosys.2026.115662_bib0029","series-title":"2016 fourth international conference on 3D vision (3DV)","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.knosys.2026.115662_bib0030","series-title":"Proceedings of the 24th ACM international conference on Multimedia","first-page":"516","article-title":"Unitbox: An advanced object detection network","author":"Yu","year":"2016"},{"key":"10.1016\/j.knosys.2026.115662_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101832","article-title":"A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging","volume":"67","author":"Xiong","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.knosys.2026.115662_bib0032","article-title":"Data from pancreas-ct. the cancer imaging archive","volume":"5","author":"Roth","year":"2016","journal-title":"IEEE Trans. Image Process."},{"issue":"10","key":"10.1016\/j.knosys.2026.115662_bib0033","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (brats)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imag."},{"key":"10.1016\/j.knosys.2026.115662_bib0034","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","first-page":"552","article-title":"Shape-aware semi-supervised 3d semantic segmentation for medical images","author":"Li","year":"2020"},{"issue":"7","key":"10.1016\/j.knosys.2026.115662_bib0035","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1038\/s42256-023-00682-w","article-title":"Uncertainty-guided dual-views for semi-supervised volumetric medical image segmentation","volume":"5","author":"Peiris","year":"2023","journal-title":"Nature Mach. Intell."}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126004028?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705126004028?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:16:01Z","timestamp":1777594561000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705126004028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5]]},"references-count":35,"alternative-id":["S0950705126004028"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115662","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KP2L: Knowledge-driven pyramid prototype learning for semi-supervised medical image segmentation","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2026.115662","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115662"}}