{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T19:19:44Z","timestamp":1777231184471,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,12]]},"DOI":"10.1007\/s11517-023-02920-0","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:02:14Z","timestamp":1694217734000},"page":"3409-3417","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Dual CNN cross-teaching semi-supervised segmentation network with multi-kernels and global contrastive loss in ACDC"],"prefix":"10.1007","volume":"61","author":[{"given":"Keming","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8278-6454","authenticated-orcid":false,"given":"Kefeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jindi","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"2920_CR1","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition pp 3431\u20133440","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"2920_CR2","first-page":"234","volume-title":"International Conference on Medical image computing and computer-assisted intervention","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, pp 234\u2013241"},{"key":"2920_CR3","doi-asserted-by":"crossref","unstructured":"Isensee F, Petersen J, Klein A et al (2018) nnu-net: self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:1809.10486","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"2920_CR4","first-page":"318","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"X Luo","year":"2021","unstructured":"Luo X, Liao W, Chen J et al (2021) Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 318\u2013329"},{"key":"2920_CR5","first-page":"253","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"W Bai","year":"2017","unstructured":"Bai W, Oktay O, Sinclair M et al (2017) Semi-supervised learning for network-based cardiac MR image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 253\u2013260"},{"issue":"10","key":"2920_CR6","first-page":"8801","volume":"35","author":"X Luo","year":"2021","unstructured":"Luo X, Chen J, Song T et al (2021) Semi-supervised medical image segmentation through dual-task consistency. Proc AAAI Conf Art Intell 35(10):8801\u20138809","journal-title":"Proc AAAI Conf Art Intell"},{"key":"2920_CR7","doi-asserted-by":"crossref","unstructured":"Yu L, Wang S, Li X et al (2019) Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp 605-613","DOI":"10.1007\/978-3-030-32245-8_67"},{"issue":"2","key":"2920_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. Workshop on challenges in representation learning, ICML 3(2):896","journal-title":"Workshop on challenges in representation learning, ICML"},{"key":"2920_CR9","unstructured":"Li X, Yu L, Chen H et al (2018) Semi-supervised skin lesion segmentation via transformation consistent self-ensembling model. arXiv preprint arXiv:1808.03887"},{"key":"2920_CR10","doi-asserted-by":"publisher","first-page":"102459","DOI":"10.1016\/j.media.2022.102459","volume":"79","author":"X Wang","year":"2011","unstructured":"Wang X, Yuan Y, Guo D et al (2011) SSA-Net: spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning. Med Image Anal 79:102459","journal-title":"Med Image Anal"},{"issue":"11","key":"2920_CR11","doi-asserted-by":"publisher","first-page":"4140","DOI":"10.1109\/JBHI.2021.3103646","volume":"25","author":"C Li","year":"2021","unstructured":"Li C, Dong L, Dou Q et al (2021) Self-ensembling co-training framework for semi-supervised COVID-19 CT segmentation. IEEE J Biomed Health Inform 25(11):4140\u20134151","journal-title":"IEEE J Biomed Health Inform"},{"key":"2920_CR12","unstructured":"Luo X, Hu M, Song T et al (2021) Semi-supervised medical image segmentation via cross teaching between CNN and Transformer. arXiv preprint arXiv:2112.04894"},{"key":"2920_CR13","unstructured":"Sajjadi M, Javanmardi M, Tasdizen T (2019) Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Advances in neural information processing systems 29"},{"key":"2920_CR14","unstructured":"Laine S, Aila T (2016) Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242"},{"key":"2920_CR15","unstructured":"Tarvainen A, Valpola H (2017) Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30"},{"key":"2920_CR16","doi-asserted-by":"crossref","unstructured":"He K, Fan H, Wu Y et al (2020) Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9729-9738","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"2920_CR17","unstructured":"Chen X, Fan H, Girshick R et al (2020) Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297"},{"key":"2920_CR18","unstructured":"Chen T, Kornblith S, Norouzi M et al (2020) A simple framework for contrastive learning of visual representations. International conference on machine learning. PMLR, 1597-1607"},{"key":"2920_CR19","first-page":"22243","volume":"33","author":"T Chen","year":"2020","unstructured":"Chen T, Kornblith S, Swersky K et al (2020) Big self-supervised models are strong semi-supervised learners. Advances in Neural Information Processing Systems 33:22243\u201322255","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2920_CR20","doi-asserted-by":"crossref","unstructured":"Zhong Y, Yuan B, Wu H et al (2021) Pixel contrastive-consistent semi-supervised semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp 7273-7282","DOI":"10.1109\/ICCV48922.2021.00718"},{"key":"2920_CR21","doi-asserted-by":"publisher","first-page":"102656","DOI":"10.1016\/j.media.2022.102656","volume":"83","author":"S Zhang","year":"2023","unstructured":"Zhang S, Zhang J, Tian B et al (2023) Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation. Med Image Anal 83:102656","journal-title":"Med Image Anal"},{"key":"2920_CR22","doi-asserted-by":"crossref","unstructured":"Xiao Z, Su Y, Deng Z et al (2022) Efficient combination of CNN and Transformer for dual-teacher uncertainty-aware guided semi-supervised medical image segmentation. Available at SSRN 4081789","DOI":"10.2139\/ssrn.4081789"},{"key":"2920_CR23","doi-asserted-by":"crossref","unstructured":"Liu Y, Wang W, Luo G et al (2022) A contrastive consistency semi-supervised left atrium segmentation model. Comput Med Imaging Graph, 2022, 99:102092","DOI":"10.1016\/j.compmedimag.2022.102092"},{"key":"2920_CR24","doi-asserted-by":"crossref","unstructured":"Wang T, Lu J, Lai Z et al (2022) Uncertainty-guided pixel contrastive learning for semi-supervised medical image segmentation. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, pp 1444\u20131450","DOI":"10.24963\/ijcai.2022\/201"},{"key":"2920_CR25","first-page":"408","volume-title":"International conference on medical image computing and computer-assisted intervention","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Yang L, Chen J et al (2017) Deep adversarial networks for biomedical image segmentation utilizing unannotated images. International conference on medical image computing and computer-assisted intervention. Springer, Cham, pp 408-416"},{"key":"2920_CR26","doi-asserted-by":"crossref","unstructured":"Vu TH, Jain H, Bucher M et al (2019) Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2517-2526","DOI":"10.1109\/CVPR.2019.00262"},{"key":"2920_CR27","doi-asserted-by":"crossref","unstructured":"Chen X, Yuan Y, Zeng G et al (2021) Semi-supervised semantic segmentation with cross pseudo supervision. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 2613-2622","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"2920_CR28","doi-asserted-by":"crossref","unstructured":"Zhao Z, Hu J, Zeng Z et al (2022) MMGL: multi-scale multi-view global-local contrastive learning for semi-supervised cardiac image segmentation. 2022 IEEE International Conference on Image Processing (ICIP) pp 401-405","DOI":"10.1109\/ICIP46576.2022.9897591"},{"issue":"11","key":"2920_CR29","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? IEEE Trans Med Imaging 37(11):2514\u20132525","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"2920_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2016.18","volume":"3","author":"MD Wilkinson","year":"2016","unstructured":"Wilkinson MD, Dumontier M, Aalbersberg IJJ et al (2016) The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3(1):1\u20139","journal-title":"Sci Data"},{"key":"2920_CR31","doi-asserted-by":"crossref","unstructured":"You C, Dai W, Min Y et al (2023) Action++: improving semi-supervised medical image segmentation with adaptive anatomical contrast. arXiv preprint arXiv:2304.02689","DOI":"10.1007\/978-3-031-43901-8_19"},{"key":"2920_CR32","doi-asserted-by":"crossref","unstructured":"Wu H, Li X, Lin Y et al (2023) Compete to win: enhancing pseudo labels for barely-supervised medical image segmentation. IEEE Trans Med Imaging","DOI":"10.1109\/TMI.2023.3279110"},{"key":"2920_CR33","unstructured":"Zhu J, Bolsterlee B, Chow BVY et al (2023) Hybrid dual mean-teacher network with double-uncertainty guidance for semi-supervised segmentation of MRI scans. arXiv preprint arXiv:2303.05126"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02920-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02920-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02920-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,23]],"date-time":"2023-12-23T03:16:24Z","timestamp":1703301384000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02920-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,9]]},"references-count":33,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["2920"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02920-0","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,9]]},"assertion":[{"value":"14 January 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 August 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 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":"This is a numerical simulation study for which no ethical approval was required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}