{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T06:06:02Z","timestamp":1771653962736,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Jiangxi Provincial Key Research and Development Program","award":["20214BBG74006"],"award-info":[{"award-number":["20214BBG74006"]}]},{"name":"Jiangxi Provincial Department of Science and Technology Project","award":["ZBG20230418031"],"award-info":[{"award-number":["ZBG20230418031"]}]},{"name":"Jiangxi Provincial Key Research and Development Program","award":["20232BBG70031"],"award-info":[{"award-number":["20232BBG70031"]}]},{"name":"Jiangxi Provincial Graduate Student Innovation Special Fund Project","award":["YC2023-S132"],"award-info":[{"award-number":["YC2023-S132"]}]},{"name":"Ganjiang New Area Major Scientific and Technological Breakthrough Project","award":["2023001"],"award-info":[{"award-number":["2023001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s11760-026-05124-9","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T11:50:52Z","timestamp":1770119452000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Intrinsic feature consistency learning based on dual-branch network for accurate semi-supervised medical image segmentation"],"prefix":"10.1007","volume":"20","author":[{"given":"Yunjun","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaohao","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Tao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaoyu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubo","family":"Gong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"issue":"2","key":"5124_CR1","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1007\/s11760-023-02893-5","volume":"18","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Han, L., Yao, B., Li, Q.: Sta-former: enhancing medical image segmentation with shrinkage triplet attention in a hybrid cnn-transformer model. SIViP 18(2), 1901\u20131910 (2024)","journal-title":"SIViP"},{"issue":"5","key":"5124_CR2","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1007\/s11760-022-02388-9","volume":"17","author":"T Huang","year":"2023","unstructured":"Huang, T., Chen, J., Jiang, L.: Ds-unext: depthwise separable convolution network with large convolutional kernel for medical image segmentation. SIViP 17(5), 1775\u20131783 (2023)","journal-title":"SIViP"},{"key":"5124_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112254","volume":"301","author":"Y Huang","year":"2024","unstructured":"Huang, Y., Han, X., Chen, M., Pan, Z.: Tggs network: A multi-task learning network for gradient-guided knowledge sharing. Knowl.-Based Syst. 301, 112254 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"5124_CR4","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems 25 (2012)"},{"key":"5124_CR5","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"5124_CR6","doi-asserted-by":"crossref","unstructured":"Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Myronenko, A., Landman, B., Roth, H.R., Xu, D.: Unetr: Transformers for 3d medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 574\u2013584 (2022)","DOI":"10.1109\/WACV51458.2022.00181"},{"key":"5124_CR7","doi-asserted-by":"crossref","unstructured":"Azad, R., Heidari, M., Shariatnia, M., Aghdam, E.K., Karimijafarbigloo, S., Adeli, E., Merhof, D.: Transdeeplab: Convolution-free transformer-based deeplab v3+ for medical image segmentation. In: Predictive Intelligence in Medicine, pp. 91\u2013102 (2022)","DOI":"10.1007\/978-3-031-16919-9_9"},{"issue":"6","key":"5124_CR8","doi-asserted-by":"publisher","first-page":"3073","DOI":"10.1007\/s11760-023-02528-9","volume":"17","author":"B Liang","year":"2023","unstructured":"Liang, B., Tang, C., Zhang, W., Xu, M., Wu, T.: N-net: an unet architecture with dual encoder for medical image segmentation. SIViP 17(6), 3073\u20133081 (2023)","journal-title":"SIViP"},{"issue":"1","key":"5124_CR9","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/s11760-024-03690-4","volume":"19","author":"R Kaur","year":"2025","unstructured":"Kaur, R., Ranade, S.K.: Du-net+: a fully convolutional neural network architecture for semantic segmentation of skin lesions. SIViP 19(1), 152 (2025)","journal-title":"SIViP"},{"issue":"5","key":"5124_CR10","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1007\/s11760-025-03917-y","volume":"19","author":"P Song","year":"2025","unstructured":"Song, P., Wu, Y.: Wfl-vnet: retinal vessel segmentation method using whole-process feature localization. SIViP 19(5), 356 (2025)","journal-title":"SIViP"},{"key":"5124_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112412","volume":"304","author":"L Qiao","year":"2024","unstructured":"Qiao, L., Wang, R., Shu, Y., Xiao, B., Xu, X., Li, B., Yang, L., Li, W., Gao, X., Lei, B.: Cmrvae: Contrastive margin-restrained variational auto-encoder for class-separated domain adaptation in cardiac segmentation. Knowl.-Based Syst. 304, 112412 (2024)","journal-title":"Knowl.-Based Syst."},{"key":"5124_CR12","doi-asserted-by":"crossref","unstructured":"Huang, Y., Chen, M., Chen, J., Pan, Z.: Dcmtl network: A double-contrast multi-task learning network for semi-supervised multi-source data classification. Expert Syst. Appl. 129062 (2025)","DOI":"10.1016\/j.eswa.2025.129062"},{"key":"5124_CR13","doi-asserted-by":"crossref","unstructured":"Souly, N., Spampinato, C., Shah, M.: Semi supervised semantic segmentation using generative adversarial network. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 5688\u20135696 (2017)","DOI":"10.1109\/ICCV.2017.606"},{"key":"5124_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, L., Chen, J., Fredericksen, M., Hughes, D.P., Chen, D.Z.: Deep adversarial networks for biomedical image segmentation utilizing unannotated images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention (2017)","DOI":"10.1007\/978-3-319-66179-7_47"},{"key":"5124_CR15","doi-asserted-by":"crossref","unstructured":"Hou, J., Ding, X., Deng, J.D.: Semi-supervised semantic segmentation of vessel images using leaking perturbations. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2625\u20132634 (2022)","DOI":"10.1109\/WACV51458.2022.00183"},{"key":"5124_CR16","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4320\u20134328 (2018)","DOI":"10.1109\/CVPR.2018.00454"},{"key":"5124_CR17","doi-asserted-by":"crossref","unstructured":"Feng, Z., Zhou, Q., Gu, Q., Tan, X., Cheng, G., Lu, X., Shi, J., Ma, L.: Dmt: Dynamic mutual training for semi-supervised learning. Pattern Recognit. 130, 108777 (2022)","DOI":"10.1016\/j.patcog.2022.108777"},{"key":"5124_CR18","unstructured":"Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C.A., Cubuk, E.D., Kurakin, A., Li, C.-L.: Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems 33, 596\u2013608 (2020)"},{"key":"5124_CR19","doi-asserted-by":"crossref","unstructured":"Mai, H., Sun, R., Zhang, T., Wu, F.: Rankmatch: Exploring the better consistency regularization for semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3391\u20133401 (2024)","DOI":"10.1109\/CVPR52733.2024.00326"},{"key":"5124_CR20","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27 (2014)"},{"key":"5124_CR21","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhuo, W., Qi, L., Shi, Y., Gao, Y.: St++: Make self-training work better for semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4268\u20134277 (2022)","DOI":"10.1109\/CVPR52688.2022.00423"},{"key":"5124_CR22","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems 30 (2017)"},{"key":"5124_CR23","unstructured":"Zhu, Y., Yang, J., Liu, S.-Q., Zhang, R.: Inherent consistent learning for accurate semi-supervised medical image segmentation. arXiv preprint arXiv:2303.14175 (2023)"},{"key":"5124_CR24","doi-asserted-by":"crossref","unstructured":"Chen, X., Yuan, Y., Zeng, G., Wang, J.: Semi-supervised semantic segmentation with cross pseudo supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"5124_CR25","unstructured":"Luo, X., Hu, M., Song, T., Wang, G., Zhang, S.: Semi-supervised medical image segmentation via cross teaching between cnn and transformer. In: International Conference on Medical Imaging with Deep Learning, pp. 820\u2013833 (2022). PMLR"},{"key":"5124_CR26","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: International Conference on Learning Representations (2017)"},{"issue":"4","key":"5124_CR27","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"J Staal","year":"2004","unstructured":"Staal, J., Abr\u00e0moff, M.D., Niemeijer, M., Viergever, M.A., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5124_CR28","doi-asserted-by":"crossref","unstructured":"Hoover, A.D., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203\u2013210 (2000)","DOI":"10.1109\/42.845178"},{"issue":"9","key":"5124_CR29","doi-asserted-by":"publisher","first-page":"2538","DOI":"10.1109\/TBME.2012.2205687","volume":"59","author":"MM Fraz","year":"2012","unstructured":"Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: An ensemble classification-based approach applied to retinal blood vessel segmentation. IEEE Trans. Biomed. Eng. 59(9), 2538\u20132548 (2012)","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"5124_CR30","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., Cervenansky, F., Yang, X., Heng, P.-A., Cetin, I., Lekadir, K., Camara, O., Ballester, M.A.G., et al.: Deep learning techniques for automatic mri cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans. Med. Imaging 37(11), 2514\u20132525 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5124_CR31","doi-asserted-by":"crossref","unstructured":"Ouali, Y., Hudelot, C., Tami, M.: Semi-supervised semantic segmentation with cross-consistency training. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"5124_CR32","doi-asserted-by":"crossref","unstructured":"Vu, T.-H., Jain, H., Bucher, M., Cord, M., Perez, P.: Advent: Adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00262"}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05124-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05124-9","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05124-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T05:25:50Z","timestamp":1771651550000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05124-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":32,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["5124"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05124-9","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"17 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":4,"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 conflicts of interest or funding.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"87"}}