{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T13:22:18Z","timestamp":1771680138267,"version":"3.50.1"},"reference-count":50,"publisher":"Wiley","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62176181"],"award-info":[{"award-number":["62176181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2024,3,15]]},"abstract":"<jats:p>Medical image segmentation is a critical task in the healthcare field. While deep learning techniques have shown promise in this area, they often require a large number of accurately labeled images. To address this issue, semisupervised learning has emerged as a potential solution by reducing the reliance on precise annotations. Among these approaches, the student-teacher framework has garnered attention, but it is limited in its reliance solely on the teacher model for information. To overcome this limitation, we propose a prototype-based mutual consistency learning (PMCL) framework. This framework utilizes two branches that learn from each other, incorporating supervision loss and consistency loss to adapt to minor data perturbations and structural differences. By employing prototype consistency learning, we are able to achieve reliable consistency loss. Our experiments on three public medical image datasets demonstrate that PMCL outperforms other state-of-the-art methods, indicating its potential in semisupervised medical image segmentation. Our framework has the potential to assist medical professionals in enhancing their diagnoses and delivering improved patient care.<\/jats:p>","DOI":"10.1155\/2024\/9928155","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T22:20:29Z","timestamp":1710541229000},"page":"1-11","source":"Crossref","is-referenced-by-count":1,"title":["Semisupervised Medical Image Segmentation through Prototype-Based Mutual Consistency Learning"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8230-482X","authenticated-orcid":true,"given":"Xinqiang","family":"Wang","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China"},{"name":"School of Software and Communication, Tianjin Sino-German University of Applied Sciences, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7951-8907","authenticated-orcid":true,"given":"Wenhuan","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2714-042X","authenticated-orcid":true,"given":"Si","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5984-3481","authenticated-orcid":true,"given":"Ke","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4289-647X","authenticated-orcid":true,"given":"Junhai","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8964-9759","authenticated-orcid":true,"given":"Jianguo","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing, Tianjin Key Lab of Cognitive Computing and Application, Tianjin University, Tianjin 300350, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","article-title":"Msrf-net: a multi-scale residual fusion network for biomedical image segmentation","author":"A. Srivastava","year":"2021"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3116265"},{"key":"3","first-page":"558","article-title":"Doubleu-net: a deep convolutional neural network for medical image segmentation","author":"D. Jha"},{"key":"4","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","author":"S. Laine"},{"key":"5","first-page":"1195","article-title":"Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results","author":"A. Tarvainen","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"6","first-page":"1135","article-title":"Semi-supervised skin lesion segmentation with learning model confidence","author":"Z. Xie"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2020.2995319"},{"key":"8","first-page":"605","article-title":"Uncertainty-aware self-ensembling model for semi-supervised 3d left atrium segmentation","author":"L. Yu"},{"key":"9","article-title":"Mutual-and self-prototype alignment for semi-supervised medical image segmentation","author":"Z. Zhang","year":"2022"},{"key":"10","first-page":"408","article-title":"Deep adversarial networks for biomedical image segmentation utilizing unannotated images","author":"Y. Zhang"},{"key":"11","first-page":"552","article-title":"Shape-aware semi-supervised 3d semantic segmentation for medical images","author":"S. Li"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"13","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","author":"O. Ronneberger"},{"key":"14","first-page":"614","article-title":"Self-loop uncertainty: a novel pseudo-label for semi-supervised medical image segmentation","author":"Y. Li"},{"key":"15","first-page":"282","article-title":"Uncertainty guided semi-supervised segmentation of retinal layers in oct images","author":"S. Sedai"},{"key":"16","article-title":"Adversarial learning for semi-supervised semantic segmentation","author":"W.-C. Hung"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2018.2858821"},{"key":"18","article-title":"Mobilenets: efficient convolutional neural networks for mobile vision applications","author":"A. G. Howard","year":"2017"},{"key":"19","first-page":"6848","article-title":"Shufflenet: an extremely efficient convolutional neural network for mobile devices","author":"X. Zhang"},{"key":"20","article-title":"Distilling the knowledge in a neural network","author":"G. Hinton","year":"2015"},{"key":"21","first-page":"4320","article-title":"Deep mutual learning","author":"Y. Zhang"},{"key":"22","first-page":"297","article-title":"Semi-supervised left atrium segmentation with mutual consistency training","author":"Y. Wu"},{"key":"23","first-page":"548","article-title":"Dual-task mutual learning for semi-supervised medical image segmentation","author":"Y. Zhang"},{"key":"24","first-page":"589","article-title":"Modality-aware mutual learning for multi-modal medical image segmentation","author":"Y. Zhang"},{"key":"25","doi-asserted-by":"publisher","DOI":"10.1109\/jbhi.2022.3162043"},{"key":"26","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-43895-0_18","article-title":"Self-aware and cross-sample prototypical learning for semi-supervised medical image segmentation","author":"Z. Zhang","year":"2023"},{"key":"27","article-title":"Prototypical networks for few-shot learning","volume":"30","author":"J. Snell","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"28","article-title":"Matching networks for one shot learning","volume":"29","author":"O. Vinyals","year":"2016","journal-title":"Advances in Neural Information Processing Systems"},{"key":"29","first-page":"214","article-title":"Optimization as a model for few-shot learning","author":"H. L. Sachin Ravi"},{"key":"30","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","author":"C. Finn"},{"key":"31","first-page":"3916","article-title":"Few-shot learning with graph neural networks","author":"J. B. Svictor Garcia"},{"key":"32","article-title":"Learning to propagate labels: transductive propagation network for few-shot learning","author":"Y. Liu"},{"key":"33","doi-asserted-by":"crossref","DOI":"10.5244\/C.31.167","article-title":"One-shot learning for semantic segmentation","author":"A. Shaban","year":"2017"},{"issue":"4","key":"34","article-title":"Few-shot semantic segmentation with prototype learning","volume":"3","author":"N. Dong","year":"2018","journal-title":"British Machine Vision Conference (BMVC)"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1109\/tcyb.2020.2992433"},{"key":"36","first-page":"9197","article-title":"Panet: few-shot image semantic segmentation with prototype alignment","author":"K. Wang"},{"key":"37","first-page":"5217","article-title":"Canet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning","author":"C. Zhang"},{"key":"38","first-page":"622","article-title":"Feature weighting and boosting for few-shot segmentation","author":"K. Nguyen"},{"key":"39","first-page":"5249","article-title":"Amp: adaptive masked proxies for few-shot segmentation","author":"M. Siam"},{"key":"40","first-page":"8334","article-title":"Adaptive prototype learning and allocation for few-shot segmentation","author":"G. Li"},{"key":"41","first-page":"142","article-title":"Part-aware prototype network for few-shot semantic segmentation","author":"Y. Liu"},{"key":"42","first-page":"762","article-title":"Self-supervision with superpixels: training few-shot medical image segmentation without annotation","author":"C. Ouyang"},{"key":"43","article-title":"Conditional networks for few-shot semantic segmentation","author":"K. Rakelly"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2015.02.007"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2015.2487997"},{"key":"46","first-page":"451","article-title":"Kvasir-seg: a segmented polyp dataset","author":"D. Jha"},{"key":"47","first-page":"225","article-title":"Resunet++: an advanced architecture for medical image segmentation","author":"D. Jha"},{"key":"48","article-title":"Pranet: parallel reverse attention network for polyp segmentation","author":"D.-P. Fan","year":"2020"},{"key":"49","article-title":"Advent: adversarial entropy minimization for domain adaptation in semantic segmentation","author":"T.-H. Vu"},{"key":"50","first-page":"3635","article-title":"Interpolation consistency training for semi-supervised learning","author":"V. Verma"}],"container-title":["International Journal of Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2024\/9928155.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2024\/9928155.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/ijis\/2024\/9928155.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T22:20:39Z","timestamp":1710541239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/ijis\/2024\/9928155\/"}},"subtitle":[],"editor":[{"given":"Yu-An","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":50,"alternative-id":["9928155","9928155"],"URL":"https:\/\/doi.org\/10.1155\/2024\/9928155","relation":{},"ISSN":["1098-111X","0884-8173"],"issn-type":[{"value":"1098-111X","type":"electronic"},{"value":"0884-8173","type":"print"}],"subject":[],"published":{"date-parts":[[2024,3,15]]}}}