{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:19:18Z","timestamp":1776979158506,"version":"3.51.4"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"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-017"},{"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"},{"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-012"},{"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-004"}],"funder":[{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["SCRC2024ZZ05TS"],"award-info":[{"award-number":["SCRC2024ZZ05TS"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52273228"],"award-info":[{"award-number":["52273228"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009002","name":"Shanghai University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"Youth Science Fund Project","doi-asserted-by":"publisher","award":["62401352,62002215"],"award-info":[{"award-number":["62401352,62002215"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"Youth Science Fund Project","doi-asserted-by":"publisher","award":["62002215"],"award-info":[{"award-number":["62002215"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"Youth Science Fund Project","doi-asserted-by":"publisher","award":["62401352"],"award-info":[{"award-number":["62401352"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.neucom.2026.133258","type":"journal-article","created":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T07:07:30Z","timestamp":1773126450000},"page":"133258","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Scribble consistency match and pixel-level prototype contrastive calibration for weakly supervised medical segmentation"],"prefix":"10.1016","volume":"681","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziming","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6630-5339","authenticated-orcid":false,"given":"Qiaochuan","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1170-202X","authenticated-orcid":false,"given":"Yuexing","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2026.133258_bib0005","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"3159","article-title":"Scribblesup: Scribble-supervised convolutional networks for semantic segmentation","author":"Lin","year":"2016"},{"key":"10.1016\/j.neucom.2026.133258_bib0010","series-title":"Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference, Munich, Germany, October 5\u20139, 2015, proceedings, part III 18","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.neucom.2026.133258_bib0015","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s12859-019-3332-1","article-title":"Microscopy cell nuclei segmentation with enhanced u-net","volume":"21","author":"Long","year":"2020","journal-title":"BMC Bioinformatics"},{"key":"10.1016\/j.neucom.2026.133258_bib0020","doi-asserted-by":"crossref","first-page":"1837","DOI":"10.1109\/JBHI.2020.2991043","article-title":"Ai in medical imaging informatics: current challenges and future directions","volume":"24","author":"Panayides","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2026.133258_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101693","article-title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation","volume":"63","author":"Tajbakhsh","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neucom.2026.133258_bib0030","series-title":"2017 IEEE International Conference on Computer Vision (ICCV)","first-page":"5689","article-title":"Semi supervised semantic segmentation using generative adversarial network","author":"Souly","year":"2017"},{"key":"10.1016\/j.neucom.2026.133258_bib0035","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1109\/TPAMI.2019.2960224","article-title":"Semi-supervised semantic segmentation with high- and low-level consistency","volume":"43","author":"Mittal","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133258_bib0040","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II 24","first-page":"318","article-title":"Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency","author":"Luo","year":"2021"},{"key":"10.1016\/j.neucom.2026.133258_bib0045","doi-asserted-by":"crossref","first-page":"507","DOI":"10.3390\/make3020026","article-title":"Going to extremes: weakly supervised medical image segmentation","volume":"3","author":"Roth","year":"2021","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.neucom.2026.133258_bib0050","doi-asserted-by":"crossref","first-page":"1990","DOI":"10.1109\/TMI.2021.3069634","article-title":"Learning to segment from scribbles using multi-scale adversarial attention gates","volume":"40","author":"Valvano","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133258_bib0055","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11656","article-title":"Cyclemix: A holistic strategy for medical image segmentation from scribble supervision","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2026.133258_bib0060","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"528","article-title":"Scribble-supervised medical image segmentation via dual-branch network and dynamically mixed pseudo labels supervision","author":"Luo","year":"2022"},{"key":"10.1016\/j.neucom.2026.133258_bib0065","series-title":"Proceedings of the 31st ACM International Conference on Multimedia","first-page":"3384","article-title":"Scribblevc: Scribble-supervised medical image segmentation with vision-class embedding","author":"Li","year":"2023"},{"key":"10.1016\/j.neucom.2026.133258_bib0070","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"35","article-title":"S 2 me: Spatial-spectral mutual teaching and ensemble learning for scribble-supervised polyp segmentation","author":"Wang","year":"2023"},{"key":"10.1016\/j.neucom.2026.133258_bib0075","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"162","article-title":"Shapepu: A new pu learning framework regularized by global consistency for scribble supervised cardiac segmentation","author":"Zhang","year":"2022"},{"key":"10.1016\/j.neucom.2026.133258_bib0080","series-title":"European conference on computer vision","first-page":"605","article-title":"Semi-supervised vision transformers","author":"Weng","year":"2022"},{"key":"10.1016\/j.neucom.2026.133258_bib0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129749","article-title":"Visual mamba-cnn for scribble-based segmentation in weakly supervised learning for photoacoustic tomography","volume":"298","author":"Zhou","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133258_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122110","article-title":"A pseudo-labeling based weakly supervised segmentation method for few-shot texture images","volume":"238","author":"Han","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2026.133258_bib0095","series-title":"International conference on medical imaging with deep learning","first-page":"820","article-title":"Semi-supervised medical image segmentation via cross teaching between cnn and transformer","author":"Luo","year":"2022"},{"key":"10.1016\/j.neucom.2026.133258_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110922","article-title":"Complementary branch fusing class and semantic knowledge for robust weakly supervised semantic segmentation","volume":"157","author":"Han","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neucom.2026.133258_bib0105","author":"Kuo"},{"key":"10.1016\/j.neucom.2026.133258_bib0110","author":"Du"},{"key":"10.1016\/j.neucom.2026.133258_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111722","article-title":"Prototype-augmented mean teacher for robust semi-supervised medical image segmentation","volume":"166","author":"Zhang","year":"2025","journal-title":"Pattern Recognition"},{"issue":"6","key":"10.1016\/j.neucom.2026.133258_bib0120","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/TMI.2024.3363190","article-title":"Scribformer: Transformer makes cnn work better for scribble-based medical image segmentation","volume":"43","author":"Li","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133258_bib0125","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.neucom.2026.133258_bib0130","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"367","article-title":"Conformer: Local features coupling global representations for visual recognition","author":"Peng","year":"2021"},{"key":"10.1016\/j.neucom.2026.133258_bib0135","series-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops","first-page":"702","article-title":"Randaugment: Practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"10.1016\/j.neucom.2026.133258_bib0140","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133258_bib0145","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"581","article-title":"Multivariate mixture model for cardiac segmentation from multi-sequence mri","author":"Zhuang","year":"2016"},{"key":"10.1016\/j.neucom.2026.133258_bib0150","doi-asserted-by":"crossref","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","article-title":"Multivariate mixture model for myocardial segmentation combining multi-source images","volume":"41","author":"Zhuang","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neucom.2026.133258_bib0155","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1818","article-title":"Normalized cut loss for weakly-supervised cnn segmentation","author":"Tang","year":"2018"},{"key":"10.1016\/j.neucom.2026.133258_bib0160","series-title":"Proceedings of the IEEE international conference on computer vision","first-page":"1529","article-title":"Conditional random fields as recurrent neural networks","author":"Zheng","year":"2015"},{"key":"10.1016\/j.neucom.2026.133258_bib0165","series-title":"Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges: 8th International Workshop, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, September 10\u201314, 2017, Revised Selected Papers 8","first-page":"111","article-title":"An exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation","author":"Baumgartner","year":"2018"},{"key":"10.1016\/j.neucom.2026.133258_bib0170","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133258_bib0175","author":"Kim"},{"key":"10.1016\/j.neucom.2026.133258_bib0180","series-title":"Proceedings of the IEEE\/CVF international conference on computer vision","first-page":"6023","article-title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","author":"Yun","year":"2019"},{"key":"10.1016\/j.neucom.2026.133258_bib0185","author":"DeVries"},{"key":"10.1016\/j.neucom.2026.133258_bib0190","series-title":"International conference on machine learning","first-page":"5275","article-title":"Puzzle mix: Exploiting saliency and local statistics for optimal mixup","author":"Kim","year":"2020"},{"key":"10.1016\/j.neucom.2026.133258_bib0195","author":"Zhang"},{"key":"10.1016\/j.neucom.2026.133258_bib0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.109762","article-title":"Qmaxvit-unet+: A query-based maxvit-unet with edge enhancement for scribble-supervised segmentation of medical images","volume":"187","author":"Nguyen-Tat","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neucom.2026.133258_bib0205","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025: 28th International Conference, Daejeon, South Korea, September 23\u201327, 2025, Proceedings, Part XVI","first-page":"194","article-title":"Effdnet: A scribble-supervised medical image segmentation method with enhanced foreground feature discrimination","author":"Liu","year":"2025"},{"key":"10.1016\/j.neucom.2026.133258_bib0210","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1109\/JBHI.2024.3404884","article-title":"Fddseg: Unleashing the power of scribble annotation for cardiac mri images through feature decomposition distillation","volume":"29","author":"Zhang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.neucom.2026.133258_bib0215","doi-asserted-by":"crossref","first-page":"3813","DOI":"10.1109\/TMI.2020.3005297","article-title":"Post-dae: anatomically plausible segmentation via post-processing with denoising autoencoders","volume":"39","author":"Larrazabal","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neucom.2026.133258_bib0220","author":"Zhang"},{"key":"10.1016\/j.neucom.2026.133258_bib0225","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnu-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226006557?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231226006557?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:30:45Z","timestamp":1776976245000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231226006557"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":45,"alternative-id":["S0925231226006557"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133258","relation":{},"ISSN":["0925-2312"],"issn-type":[{"value":"0925-2312","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Scribble consistency match and pixel-level prototype contrastive calibration for weakly supervised medical segmentation","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2026.133258","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"133258"}}