{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T08:18:42Z","timestamp":1772093922289,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T00:00:00Z","timestamp":1768953600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Joint Funds for the Innovation of Science and Technology, Fujian province","award":["2023Y9123"],"award-info":[{"award-number":["2023Y9123"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s00500-025-10967-4","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T10:26:52Z","timestamp":1768991212000},"page":"1413-1429","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CCNet: Cross-teaching semi-supervised ultrasound image segmentation with hybrid convolutional kernels"],"prefix":"10.1007","volume":"30","author":[{"given":"Huabiao","family":"Zhou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7596-3299","authenticated-orcid":false,"given":"Yanmin","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Zhikui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Minling","family":"Zhuo","sequence":"additional","affiliation":[]},{"given":"Qingfu","family":"Qian","sequence":"additional","affiliation":[]},{"given":"Wanyuan","family":"Gong","sequence":"additional","affiliation":[]},{"given":"ZhongWei","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Weiwei","family":"Lin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"10967_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dib.2019.104863","volume":"28","author":"W Al-Dhabyani","year":"2020","unstructured":"Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A (2020) Dataset of breast ultrasound images. Data Brief 28:104863. https:\/\/doi.org\/10.1016\/j.dib.2019.104863","journal-title":"Data Brief"},{"key":"10967_CR2","doi-asserted-by":"publisher","unstructured":"Alonso I, Sabater A, Ferstl D, Montesano L, Murillo AC (2021) Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8199\u20138208. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00811","DOI":"10.1109\/ICCV48922.2021.00811"},{"key":"10967_CR3","unstructured":"Berthelot D, Carlini N, Goodfellow I, Papernot N, Oliver A, Raffel CA (2019) Mixmatch: A holistic approach to semi-supervised learning. Advances in neural information processing systems 32"},{"key":"10967_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2020.102027","volume":"61","author":"M Byra","year":"2020","unstructured":"Byra M, Jarosik P, Szubert A, Galperin M, Ojeda-Fournier H, Olson L, O\u2019Boyle M, Comstock C, Andre M (2020) Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. Biomed Signal Process Control 61:102027. https:\/\/doi.org\/10.1016\/j.bspc.2020.102027","journal-title":"Biomed Signal Process Control"},{"key":"10967_CR5","unstructured":"Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, Murrey B, Myronenko A, Zhao C, Yang D et al (2022) Monai: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701"},{"key":"10967_CR6","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"10967_CR7","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 248\u2013255. Ieee","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"1","key":"10967_CR8","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1007\/s10489-022-03642-w","volume":"53","author":"T Dhamija","year":"2023","unstructured":"Dhamija T, Gupta A, Gupta S, Anjum Katarya R, Singh G (2023) Semantic segmentation in medical images through transfused convolution and transformer networks. Appl Intell 53(1):1132\u20131148","journal-title":"Appl Intell"},{"issue":"19","key":"10967_CR9","doi-asserted-by":"publisher","first-page":"27915","DOI":"10.1007\/s11042-019-07884-8","volume":"78","author":"H Fan","year":"2019","unstructured":"Fan H, Meng F, Liu Y, Kong F, Ma J, Lv Z (2019) A novel breast ultrasound image automated segmentation algorithm based on seeded region growing integrating gradual equipartition threshold. Multimedia Tools Appl 78(19):27915\u201327932. https:\/\/doi.org\/10.1007\/s11042-019-07884-8","journal-title":"Multimedia Tools Appl"},{"key":"10967_CR10","first-page":"1140","volume":"35","author":"M-H Guo","year":"2022","unstructured":"Guo M-H, Lu C-Z, Hou Q, Liu Z, Cheng M-M, Hu S-M (2022) Segnext: rethinking convolutional attention design for semantic segmentation. Adv Neural Inf Process Syst 35:1140\u20131156","journal-title":"Adv Neural Inf Process Syst"},{"key":"10967_CR11","doi-asserted-by":"publisher","unstructured":"Gupta S, Sunkaria RK (2017) An improved edge detection algorithm using a modified discrete wavelet transform based on morphological thinner for noisy medical images. In: 2017 Fourth International Conference on Image Information Processing (ICIIP), pp 1\u20136. https:\/\/doi.org\/10.1109\/ICIIP.2017.8313791","DOI":"10.1109\/ICIIP.2017.8313791"},{"key":"10967_CR12","doi-asserted-by":"publisher","unstructured":"He K, Fan H, Wu Y, Xie S, Girshick R (2020) Momentum Contrast for Unsupervised Visual Representation Learning. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 9726\u20139735. https:\/\/doi.org\/10.1109\/CVPR42600.2020.00975","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10967_CR13","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5786","key":"10967_CR14","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science (New York, N.Y.) 313(5786):504\u2013507. https:\/\/doi.org\/10.1126\/science.1127647","journal-title":"Science (New York, N.Y.)"},{"key":"10967_CR15","doi-asserted-by":"crossref","unstructured":"Howard A, Zhmoginov A, Chen L-C, Sandler M, Zhu M (2018) Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation. In: Proc. CVPR, pp. 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"10967_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107840","volume":"169","author":"R Jiao","year":"2023","unstructured":"Jiao R, Zhang Y, Ding L, Xue B, Zhang J, Cai R, Jin C (2023) Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. Comput Biol Med 169:107840","journal-title":"Comput Biol Med"},{"key":"10967_CR17","unstructured":"Joubbi S, Ciano G, Cardamone D, Maccari G, Medini D (2023) Crossct: Cnn and transformer cross\u2013teaching for multimodal image cell segmentation. In: Competitions in Neural Information Processing Systems, pp. 1\u201314. PMLR"},{"issue":"6","key":"10967_CR18","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60(6):84\u201390. https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun ACM"},{"key":"10967_CR19","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388. https:\/\/doi.org\/10.1016\/j.media.2017.07.005","journal-title":"Med Image Anal"},{"issue":"2","key":"10967_CR20","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","volume":"5","author":"S Liu","year":"2019","unstructured":"Liu S, Wang Y, Yang X, Lei B, Liu L, Li SX, Ni D, Wang T (2019) Deep learning in medical ultrasound analysis: a review. Eng 5(2):261\u2013275","journal-title":"Eng"},{"key":"10967_CR21","doi-asserted-by":"publisher","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11966\u201311976. https:\/\/doi.org\/10.1109\/CVPR52688.2022.01167","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"10967_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102517","volume":"80","author":"X Luo","year":"2022","unstructured":"Luo X, Wang G, Liao W, Chen J, Song T, Chen Y, Zhang S, Metaxas DN, Zhang S (2022) Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Med Image Anal 80:102517","journal-title":"Med Image Anal"},{"key":"10967_CR23","unstructured":"Luo X, Hu M, Song T, Wang G, Zhang S (2022) Semi-supervised medical image segmentation via cross teaching between cnn and transformer. In: International Conference on Medical Imaging with Deep Learning, pp 820\u2013833. PMLR"},{"key":"10967_CR24","unstructured":"Mehta S, Rastegari M (2022) MobileViT: Light-weight, general-purpose, and mobile-friendly vision transformer"},{"key":"10967_CR25","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.compbiomed.2017.11.018","volume":"92","author":"KM Meiburger","year":"2018","unstructured":"Meiburger KM, Acharya UR, Molinari F (2018) Automated localization and segmentation techniques for B-mode ultrasound images: a review. Comput Biol Med 92:210\u2013235. https:\/\/doi.org\/10.1016\/j.compbiomed.2017.11.018","journal-title":"Comput Biol Med"},{"key":"10967_CR26","doi-asserted-by":"crossref","unstructured":"Oksuz K, Cam BC, Akbas E, Kalkan S (2018) Localization recall precision (lrp): A new performance metric for object detection. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 504\u2013519","DOI":"10.1007\/978-3-030-01234-2_31"},{"key":"10967_CR27","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L et al (2019) Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32"},{"issue":"17","key":"10967_CR28","doi-asserted-by":"publisher","first-page":"8317","DOI":"10.1007\/s00500-022-07235-0","volume":"26","author":"C Qin","year":"2022","unstructured":"Qin C, Wu Y, Zeng J, Tian L, Zhai Y, Li F, Zhang X (2022) Joint transformer and multi-scale cnn for dce-mri breast cancer segmentation. Soft Comput 26(17):8317\u20138334","journal-title":"Soft Comput"},{"key":"10967_CR29","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10967_CR30","doi-asserted-by":"crossref","unstructured":"Roy S, Koehler G, Ulrich C, Baumgartner M, Petersen J, Isensee F, Jaeger PF, Maier-Hein KH (2023) Mednext: transformer-driven scaling of convnets for medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 405\u2013415. Springer","DOI":"10.1007\/978-3-031-43901-8_39"},{"key":"10967_CR31","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"key":"10967_CR32","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn K, Berthelot D, Carlini N, Zhang Z, Zhang H, Raffel CA, Cubuk ED, Kurakin A, Li C-L (2020) Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv Neural Inf Process Syst 33:596\u2013608","journal-title":"Adv Neural Inf Process Syst"},{"key":"10967_CR33","doi-asserted-by":"publisher","unstructured":"Tang F, Wang L, Ning C, Xian M, Ding J (2023) CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. https:\/\/doi.org\/10.1109\/ISBI53787.2023.10230609","DOI":"10.1109\/ISBI53787.2023.10230609"},{"key":"10967_CR34","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2201.09792","author":"A Trockman","year":"2022","unstructured":"Trockman A, Kolter JZ (2022) Patches are all you need? arXiv. https:\/\/doi.org\/10.48550\/arXiv.2201.09792","journal-title":"arXiv"},{"key":"10967_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530","volume":"81","author":"Y Wu","year":"2022","unstructured":"Wu Y, Ge Z, Zhang D, Xu M, Zhang L, Xia Y, Cai J (2022) Mutual consistency learning for semi-supervised medical image segmentation. Med Image Anal 81:102530. https:\/\/doi.org\/10.1016\/j.media.2022.102530","journal-title":"Med Image Anal"},{"issue":"2","key":"10967_CR36","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1109\/TMI.2023.3314430","volume":"43","author":"H Wu","year":"2023","unstructured":"Wu H, Zhang B, Chen C, Qin J (2023) Federated semi-supervised medical image segmentation via prototype-based pseudo-labeling and contrastive learning. IEEE Trans Med Imaging 43(2):649\u2013661","journal-title":"IEEE Trans Med Imaging"},{"key":"10967_CR37","doi-asserted-by":"publisher","unstructured":"Wu Z, Shi X, Lin G, Cai J (2021) Learning Meta-class Memory for Few-Shot Semantic Segmentation. In: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 497\u2013506. https:\/\/doi.org\/10.1109\/ICCV48922.2021.00056","DOI":"10.1109\/ICCV48922.2021.00056"},{"key":"10967_CR38","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2203.01324","author":"Y Wu","year":"2022","unstructured":"Wu Y, Wu Z, Wu Q, Ge Z, Cai J (2022) Exploring smoothness and class-separation for semi-supervised medical image segmentation. arXiv. https:\/\/doi.org\/10.48550\/arXiv.2203.01324","journal-title":"arXiv"},{"key":"10967_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106626","volume":"154","author":"Q Xu","year":"2023","unstructured":"Xu Q, Ma Z, Na H, Duan W (2023) Dcsau-net: a deeper and more compact split-attention u-net for medical image segmentation. Comput Biol Med 154:106626","journal-title":"Comput Biol Med"},{"key":"10967_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101880","volume":"107","author":"MH Yap","year":"2020","unstructured":"Yap MH, Goyal M, Osman F, Mart\u00ed R, Denton E, Juette A, Zwiggelaar R (2020) Breast ultrasound region of interest detection and lesion localisation. Artif Intell Med 107:101880. https:\/\/doi.org\/10.1016\/j.artmed.2020.101880","journal-title":"Artif Intell Med"},{"issue":"17","key":"10967_CR41","doi-asserted-by":"publisher","first-page":"19990","DOI":"10.1007\/s10489-023-04570-z","volume":"53","author":"Z Yu","year":"2023","unstructured":"Yu Z, Lee F, Chen Q (2023) Hct-net: hybrid cnn-transformer model based on a neural architecture search network for medical image segmentation. Appl Intell 53(17):19990\u201320006","journal-title":"Appl Intell"},{"key":"10967_CR42","doi-asserted-by":"publisher","unstructured":"Yu L, Wang S, Li X, Fu C-W, Heng P-A (2019) Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation. In: Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A (eds) Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019, pp 605\u2013613. Springer, Cham https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"10967_CR43","doi-asserted-by":"crossref","unstructured":"Yu Q, Xi N, Yuan J, Zhou Z, Dang K, Ding X (2023) Source-free domain adaptation for medical image segmentation via prototype-anchored feature alignment and contrastive learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 3\u201312. Springer","DOI":"10.1007\/978-3-031-43990-2_1"},{"key":"10967_CR44","unstructured":"Yu R, Zhang Y, Tian Y, Liu Z, Li X, Gao J (2024) Cp-unet: Contour-based probabilistic model for medical ultrasound images segmentation. arXiv preprint arXiv:2411.14250"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10967-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-025-10967-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-025-10967-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T07:29:36Z","timestamp":1772090976000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-025-10967-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,21]]},"references-count":44,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["10967"],"URL":"https:\/\/doi.org\/10.1007\/s00500-025-10967-4","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,21]]},"assertion":[{"value":"3 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 January 2026","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 that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was approved by the Ethics Committee of Fujian Medical University Union Hospital, with the approval number 2023KY115. The research was conducted using anonymized medical ultrasound images, adhering to data privacy and security standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This study utilized anonymized medical ultrasound image data in compliance with relevant ethical standards. No direct human or animal experimentation was involved.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human Participants and\/or Animals"}}]}}