{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:40:57Z","timestamp":1775821257340,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:00:00Z","timestamp":1698192000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["202203021211177"],"award-info":[{"award-number":["202203021211177"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s11517-023-02946-4","type":"journal-article","created":{"date-parts":[[2023,10,25]],"date-time":"2023-10-25T00:02:02Z","timestamp":1698192122000},"page":"405-421","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HPFG: semi-supervised medical image segmentation framework based on hybrid pseudo-label and feature-guiding"],"prefix":"10.1007","volume":"62","author":[{"given":"Feixiang","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ailian","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cimei","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,25]]},"reference":[{"key":"2946_CR1","doi-asserted-by":"publisher","unstructured":"Raghu M, Zhang C, Kleinberg J, et al (2019) Transfusion: understanding transfer learning for medical imaging[J]. Adv Neural Inform Process Syst 32. https:\/\/doi.org\/10.48550\/arXiv.1902.07208","DOI":"10.48550\/arXiv.1902.07208"},{"key":"2946_CR2","doi-asserted-by":"publisher","unstructured":"You C, Zhao R, Liu F, et al (2022) Class-aware generative adversarial transformers for medical image segmentation[J]. arXiv preprint arXiv:2201.10737. https:\/\/doi.org\/10.48550\/arXiv.2201.10737","DOI":"10.48550\/arXiv.2201.10737"},{"key":"2946_CR3","doi-asserted-by":"publisher","unstructured":"Isensee F, Petersen J, Klein A, et al (2018) nnu-net: self-adapting framework for u-net-based medical image segmentation[J]. arXiv preprint arXiv:1809.10486. https:\/\/doi.org\/10.1007\/978-3-658-25326-4_7","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"2946_CR4","doi-asserted-by":"publisher","unstructured":"Luo X, Chen J, Song T, et al (2021) Semi-supervised medical image segmentation through dual-task consistency[C]. Proc AAAI Conf Artif Intell 35(10):8801\u20138809. https:\/\/doi.org\/10.48550\/arXiv.2009.04448","DOI":"10.48550\/arXiv.2009.04448"},{"key":"2946_CR5","doi-asserted-by":"publisher","unstructured":"Basak H, Bhattacharya R, Hussain R, et al (2022) An embarrassingly simple consistency regularization method for semi-supervised medical image segmentation[J]. arXiv preprint arXiv:2202.00677. https:\/\/doi.org\/10.48550\/arXiv.2202.00677","DOI":"10.48550\/arXiv.2202.00677"},{"key":"2946_CR6","doi-asserted-by":"publisher","first-page":"102530","DOI":"10.48550\/arXiv.2112.02508","volume":"81","author":"Y Wu","year":"2022","unstructured":"Wu Y, Ge Z, Zhang D et al (2022) Mutual consistency learning for semi-supervised medical image segmentation[J]. Med Image Anal 81:102530. https:\/\/doi.org\/10.48550\/arXiv.2112.02508","journal-title":"Med Image Anal"},{"key":"2946_CR7","doi-asserted-by":"publisher","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results[J]. Adv Neural Inform Process Syst 30. https:\/\/doi.org\/10.48550\/arXiv.1703.01780","DOI":"10.48550\/arXiv.1703.01780"},{"issue":"2","key":"2946_CR8","first-page":"896","volume":"3","author":"DH Lee","year":"2013","unstructured":"Lee DH (2013) Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks[C]\/\/Workshop on challenges in representation learning. ICML 3(2):896","journal-title":"ICML"},{"key":"2946_CR9","doi-asserted-by":"publisher","unstructured":"Zou Y, Zhang Z, Zhang H, et al (2020) Pseudoseg: designing pseudo labels for semantic segmentation[J]. arXiv preprint arXiv:2010.09713. https:\/\/doi.org\/10.48550\/arXiv.2010.09713","DOI":"10.48550\/arXiv.2010.09713"},{"key":"2946_CR10","doi-asserted-by":"publisher","first-page":"596","DOI":"10.48550\/arXiv.2001.07685","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn K, Berthelot D, Carlini N et al (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence[J]. Adv Neural Inform Process Syst 33:596\u2013608. https:\/\/doi.org\/10.48550\/arXiv.2001.07685","journal-title":"Adv Neural Inform Process Syst"},{"key":"2946_CR11","doi-asserted-by":"publisher","first-page":"102447","DOI":"10.1016\/j.media.2022.102447","volume":"79","author":"K Wang","year":"2022","unstructured":"Wang K, Zhan B, Zu C et al (2022) Semi-supervised medical image segmentation via a tripled-uncertainty guided mean teacher model with contrastive learning[J]. Med Image Anal 79:102447. https:\/\/doi.org\/10.1016\/j.media.2022.102447","journal-title":"Med Image Anal"},{"key":"2946_CR12","doi-asserted-by":"publisher","first-page":"102092","DOI":"10.1016\/j.compmedimag.2022.102092","volume":"99","author":"Y Liu","year":"2022","unstructured":"Liu Y, Wang W, Luo G et al (2022) A contrastive consistency semi-supervised left atrium segmentation model[J]. Comput Med Imaging Graph 99:102092. https:\/\/doi.org\/10.1016\/j.compmedimag.2022.102092","journal-title":"Comput Med Imaging Graph"},{"key":"2946_CR13","doi-asserted-by":"publisher","unstructured":"You C, Zhao R, Staib L H, et al (2022) Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation[C]\/\/Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part IV. Cham: Springer Nature Switzerland 639-652.https:\/\/doi.org\/10.1007\/978-3-031-16440-8_61","DOI":"10.1007\/978-3-031-16440-8_61"},{"key":"2946_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3225687","author":"T Lei","year":"2022","unstructured":"Lei T, Zhang D, Du X et al (2022) Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network[J]. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/TMI.2022.3225687","journal-title":"IEEE Trans Med Imaging"},{"issue":"8","key":"2946_CR15","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","unstructured":"Miyato T, Maeda S, Koyama M et al (2018) Virtual adversarial training: a regularization method for supervised and semi-supervised learning[J]. IEEE Trans Pattern Anal Mach Intell 41(8):1979\u20131993. https:\/\/doi.org\/10.1109\/TPAMI.2018.2858821","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2946_CR16","doi-asserted-by":"publisher","unstructured":"Qiao S, Shen W, Zhang Z, et al (2018) Deep co-training for semi-supervised image recognition[C]. Proc Eur Conf Comput Vision (ECCV) 135\u2013152. https:\/\/doi.org\/10.48550\/arXiv.1803.05984","DOI":"10.48550\/arXiv.1803.05984"},{"key":"2946_CR17","doi-asserted-by":"publisher","unstructured":"Wang Z, Li T, Zheng J Q, et al (2023) When CNN meet with ViT: towards semi-supervised learning for multi-class medical image semantic segmentation[C]. Computer Vision\u2013ECCV 2022 Workshops: Tel Aviv, Israel, October 23\u201327, 2022, Proceedings, Part VII. Cham: Springer Nature Switzerland 424-441.https:\/\/doi.org\/10.1007\/978-3-031-25082-8_28","DOI":"10.1007\/978-3-031-25082-8_28"},{"key":"2946_CR18","doi-asserted-by":"publisher","first-page":"102026","DOI":"10.1016\/j.compmedimag.2021.102026","volume":"95","author":"M Yeung","year":"2022","unstructured":"Yeung M, Sala E, Sch\u00f6nlieb CB et al (2022) Unified focal loss: generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation[J]. Comput Med Imaging Graph 95:102026. https:\/\/doi.org\/10.1016\/j.compmedimag.2021.102026","journal-title":"Comput Med Imaging Graph"},{"key":"2946_CR19","first-page":"22106","volume":"34","author":"H Hu","year":"2021","unstructured":"Hu H, Wei F, Hu H et al (2021) Semi-supervised semantic segmentation via adaptive equalization learning[J]. Adv Neural Inform Process Syst 34:22106\u201322118","journal-title":"Adv Neural Inform Process Syst"},{"issue":"2","key":"2946_CR20","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato SG III, McLennan G, Bidaut L et al (2011) The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans[J]. Med Phys 38(2):915\u2013931. https:\/\/doi.org\/10.1118\/1.3528204","journal-title":"Med Phys"},{"key":"2946_CR21","doi-asserted-by":"publisher","unstructured":"Chen X, Yuan Y, Zeng G, et al (2021) Semi-supervised semantic segmentation with cross pseudo supervision[C]. Proc IEEE\/CVF Conf Comput Vision Pattern Recog 2613\u20132622. https:\/\/doi.org\/10.48550\/arXiv.2106.01226","DOI":"10.48550\/arXiv.2106.01226"},{"key":"2946_CR22","doi-asserted-by":"publisher","unstructured":"Luo X, Hu M, Song T, et al (2022) Semi-supervised medical image segmentation via cross teaching between CNN and transformer[C]. Int Conf Med Imaging Deep Learn PMLR 820\u2013833. https:\/\/doi.org\/10.48550\/arXiv.2112.04894","DOI":"10.48550\/arXiv.2112.04894"},{"key":"2946_CR23","doi-asserted-by":"publisher","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale[J]. arXiv preprint arXiv:2010.11929. https:\/\/doi.org\/10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"2946_CR24","doi-asserted-by":"publisher","unstructured":"Shen Z, Cao P, Yang H, et al (2023) Co-training with high-confidence pseudo labels for semi-supervised medical image segmentation[J]. arXiv preprint arXiv:2301.04465. https:\/\/doi.org\/10.48550\/arXiv.2301.04465","DOI":"10.48550\/arXiv.2301.04465"},{"key":"2946_CR25","doi-asserted-by":"publisher","first-page":"102458","DOI":"10.1016\/j.media.2022.102458","volume":"79","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Tian C, Bai HX, Jiao Z, Tian X (2022) Discriminaive error prediction network for semi-supervised colon gland segmentation. Med Image Anal 79:102458. https:\/\/doi.org\/10.1016\/j.media.2022.102458","journal-title":"Med Image Anal"},{"key":"2946_CR26","doi-asserted-by":"publisher","unstructured":"Zhao X, Qi Z, Wang S, et al (2023) RCPS: Rectified contrastive pseudo supervision for semi-supervised medical image segmentation[J]. arXiv preprint arXiv:2301.05500. https:\/\/doi.org\/10.48550\/arXiv.2301.05500","DOI":"10.48550\/arXiv.2301.05500"},{"key":"2946_CR27","doi-asserted-by":"publisher","unstructured":"Wang X, Zhang R, Shen C, et al (2021) Dense contrastive learning for self-supervised visual pre-training[C]. Proc IEEE\/CVF Conf Comput Vision Pattern Recog 3024\u20133033. https:\/\/doi.org\/10.48550\/arXiv.2011.09157","DOI":"10.48550\/arXiv.2011.09157"},{"key":"2946_CR28","doi-asserted-by":"publisher","unstructured":"Chen T, Kornblith S, Norouzi M, et al (2020) A simple framework for contrastive learning of visual representations[C]. Int Conf Mach Learn PMLR 1597\u20131607. https:\/\/doi.org\/10.48550\/arXiv.2002.05709","DOI":"10.48550\/arXiv.2002.05709"},{"key":"2946_CR29","doi-asserted-by":"publisher","unstructured":"He K, Fan H, Wu Y, et al (2020) Momentum contrast for unsupervised visual representation learning[C]. Proc IEEE\/CVF Conf Comput Vision Pattern Recog 9729\u20139738. https:\/\/doi.org\/10.48550\/arXiv.1911.05722","DOI":"10.48550\/arXiv.1911.05722"},{"key":"2946_CR30","doi-asserted-by":"publisher","first-page":"12546","DOI":"10.48550\/arXiv.2006.10511","volume":"33","author":"K Chaitanya","year":"2020","unstructured":"Chaitanya K, Erdil E, Karani N et al (2020) Contrastive learning of global and local features for medical image segmentation with limited annotations[J]. Adv Neural Inform Process Syst 33:12546\u201312558. https:\/\/doi.org\/10.48550\/arXiv.2006.10511","journal-title":"Adv Neural Inform Process Syst"},{"key":"2946_CR31","doi-asserted-by":"publisher","unstructured":"Hu X, Zeng D, Xu X, et al (2021) Semi-supervised contrastive learning for label-efficient medical image segmentation[C]\/\/Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II 24. Springer International Publishing 481-490.https:\/\/doi.org\/10.1007\/978-3-030-87196-3_45","DOI":"10.1007\/978-3-030-87196-3_45"},{"key":"2946_CR32","doi-asserted-by":"publisher","unstructured":"Chaitanya K, Erdil E, Karani N, et al (2023) Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation[J]. Med Image Anal 102792. https:\/\/doi.org\/10.1016\/j.media.2023.102792","DOI":"10.1016\/j.media.2023.102792"},{"key":"2946_CR33","doi-asserted-by":"publisher","unstructured":"Wu Y, Wu Z, Wu Q, et al (2022) Exploring smoothness and class-separation for semi-supervised medical image segmentation[C]\/\/Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2022: 25th International Conference, Singapore, September 18\u201322, 2022, Proceedings, Part V. Cham: Springer Nature Switzerland 34-43.https:\/\/doi.org\/10.1007\/978-3-031-16443-9_4","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"2946_CR34","doi-asserted-by":"publisher","unstructured":"Wang T, Lu J, Lai Z, et al (2022) Uncertainty-guided pixel contrastive learning for semi-supervised medical image segmentation[C]. Proc Thirty-First Int Joint Conf Artif Intell IJCAI 1444\u20131450. https:\/\/doi.org\/10.1142\/S0129065722500162","DOI":"10.1142\/S0129065722500162"},{"key":"2946_CR35","doi-asserted-by":"publisher","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation[C]\/\/Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing 234-241.https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2946_CR36","doi-asserted-by":"publisher","unstructured":"Yun S, Han D, Oh S J, et al (2019) Cutmix: regularization strategy to train strong classifiers with localizable features[C]\/\/Proceedings of the IEEE\/CVF international conference on computer vision 6023\u20136032. https:\/\/doi.org\/10.48550\/arXiv.1905.04899","DOI":"10.48550\/arXiv.1905.04899"},{"key":"2946_CR37","doi-asserted-by":"publisher","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-Net: fully convolutional neural networks for volumetric medical image segmentation[C]\/\/2016 fourth international conference on 3D vision (3DV). IEEE 565\u2013571. https:\/\/doi.org\/10.1109\/3DV.2016.79","DOI":"10.1109\/3DV.2016.79"},{"key":"2946_CR38","doi-asserted-by":"publisher","unstructured":"Oord A, Li Y, Vinyals O (2018) Representation learning with contrastive predictive coding[J]. arXiv preprint arXiv:1807.03748. https:\/\/doi.org\/10.48550\/arXiv.1807.03748","DOI":"10.48550\/arXiv.1807.03748"},{"issue":"11","key":"2946_CR39","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard O, Lalande A, Zotti C et al (2018) Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?[J]. IEEE Trans Med Imaging 37(11):2514\u20132525. https:\/\/doi.org\/10.1109\/TMI.2018.2837502","journal-title":"IEEE Trans Med Imaging"},{"key":"2946_CR40","doi-asserted-by":"publisher","unstructured":"Codella N C F, Gutman D, Celebi M E, et al (2018) 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)[C]\/\/2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE 168-172.https:\/\/doi.org\/10.1109\/ISBI.2018.8363547","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"2946_CR41","doi-asserted-by":"publisher","unstructured":"Selvaraju R R, Cogswell M, Das A, et al (2017) Grad-cam: visual explanations from deep networks via gradient-based localization[C]. Proc IEEE Int Conf Comput Vision 618\u2013626. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","DOI":"10.1007\/s11263-019-01228-7"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02946-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02946-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02946-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T06:08:17Z","timestamp":1705471697000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02946-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,25]]},"references-count":41,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["2946"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02946-4","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,25]]},"assertion":[{"value":"20 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}