{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T21:19:56Z","timestamp":1776979196986,"version":"3.51.4"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":["SIViP"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11760-026-05231-7","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T20:43:18Z","timestamp":1775680998000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A noisy label learning method based on label contrastive learning and dynamic correction"],"prefix":"10.1007","volume":"20","author":[{"given":"Siyi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunpeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zengxu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"5231_CR1","doi-asserted-by":"crossref","unstructured":"Veit, A., Alldrin, N., Chechik, G., et al.: Learning from noisy large-scale datasets with minimal supervision. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6575\u20136583 (2017).","DOI":"10.1109\/CVPR.2017.696"},{"key":"5231_CR2","doi-asserted-by":"publisher","unstructured":"Zheng, G., Awadallah, A. H., Dumais, S.: Meta label correction for noisy label learning. In\u00a0Proceedings of the AAAI Conference on Artificial Intelligence, 35(12), pp. 11053\u201311061 https:\/\/doi.org\/10.1609\/aaai.v35i12.17319 (2021)","DOI":"10.1609\/aaai.v35i12.17319"},{"key":"5231_CR3","doi-asserted-by":"crossref","unstructured":"Yi, L., Liu, S., She, Q., et al.: On learning contrastive representations for learning with noisy labels. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16682\u201316691 (2022).","DOI":"10.1109\/CVPR52688.2022.01618"},{"key":"5231_CR4","doi-asserted-by":"publisher","unstructured":"He, Z., Wang, Y., Yang, Y., et al.: Double correction framework for denoising recommendation. In\u00a0Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), pp. 1062\u20131072 https:\/\/doi.org\/10.1145\/3637528.3671692 (2024)","DOI":"10.1145\/3637528.3671692"},{"key":"5231_CR5","doi-asserted-by":"publisher","unstructured":"Xu, J., Chen, Z., Quek, T. Q. S., et al.: FedCorr: Multi-stage federated learning for label noise correction. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https:\/\/doi.org\/10.48550\/arXiv.2204.04677 (2022)","DOI":"10.48550\/arXiv.2204.04677"},{"key":"5231_CR6","doi-asserted-by":"crossref","unstructured":"Liu, S., Liu, K., Zhu, W., et al.: Adaptive early-learning correction for segmentation from noisy annotations.\u00a0In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2606\u20132616 (2022).","DOI":"10.1109\/CVPR52688.2022.00263"},{"key":"5231_CR7","doi-asserted-by":"crossref","unstructured":"Ortego, D., Arazo, E., Albert, P., et al.: Multi-objective interpolation training for robustness to label noise. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6606\u20136615 (2021).","DOI":"10.1109\/CVPR46437.2021.00654"},{"issue":"6","key":"5231_CR8","doi-asserted-by":"publisher","first-page":"1533","DOI":"10.1109\/TMI.2022.3141425","volume":"41","author":"L Ju","year":"2022","unstructured":"Ju, L., Wang, X., Wang, L., et al.: Improving medical image classification with label noise using dual-uncertainty estimation. IEEE Trans. Med. Imaging 41(6), 1533\u20131546 (2022). https:\/\/doi.org\/10.1109\/TMI.2022.3141425","journal-title":"IEEE Trans. Med. Imaging"},{"key":"5231_CR9","doi-asserted-by":"publisher","unstructured":"Xia, X., Liu, T., Han, B., et al Sample selection with uncertainty of losses for learning with noisy labels. arXiv:2106.00445. (2021) https:\/\/doi.org\/10.48550\/arXiv.2106.00445","DOI":"10.48550\/arXiv.2106.00445"},{"key":"5231_CR10","doi-asserted-by":"crossref","unstructured":"Li, S., Xia, X., Ge, S., et al.: Selective-supervised contrastive learning with noisy labels. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 316\u2013325 (2022).","DOI":"10.1109\/CVPR52688.2022.00041"},{"key":"5231_CR11","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1016\/j.ins.2020.10.013","volume":"553","author":"B Chen","year":"2021","unstructured":"Chen, B., Xia, S., Chen, Z., et al.: RSMOTE: a self-adaptive robust SMOTE for imbalanced problems with label noise. Inf. Sci. 553, 397\u2013428 (2021). https:\/\/doi.org\/10.1016\/j.ins.2020.10.013","journal-title":"Inf. Sci."},{"key":"5231_CR12","doi-asserted-by":"publisher","unstructured":"Han, B., Yao, Q., Yu, X., et al.: Co-teaching Robust training of deep neural networks with extremely noisy labels. Adv. Neural Inf. Process. Syst (2018). https:\/\/doi.org\/10.48550\/arXiv.1804.06872","DOI":"10.48550\/arXiv.1804.06872"},{"key":"5231_CR13","doi-asserted-by":"publisher","unstructured":"Zhang, H., Cisse, M., Dauphin, Y. N et al.: MixUp: Beyond empirical risk minimization. arXiv:1710.09412 (2017). https:\/\/doi.org\/10.48550\/arXiv.1710.09412","DOI":"10.48550\/arXiv.1710.09412"},{"key":"5231_CR14","doi-asserted-by":"publisher","unstructured":"Lee, K.-H., He, X., Zhang, L., et al.: CleanNet: Transfer learning for scalable image classifier training with label noise. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5447\u20135456 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00571","DOI":"10.1109\/CVPR.2018.00571"},{"key":"5231_CR15","doi-asserted-by":"publisher","unstructured":"Grill, J.-B., Strub, F., Altch\u00e9, F., et al.: Bootstrap your own latent: A new approach to self-supervised learning. Adv Neural Inf Process Syst 33: 21271\u201321284 (2020). https:\/\/doi.org\/10.48550\/arXiv.2006.07733","DOI":"10.48550\/arXiv.2006.07733"},{"key":"5231_CR16","doi-asserted-by":"publisher","unstructured":"Chen, X., Fan, H., Girshick, R., et al.: Improved baselines with momentum contrastive learning. arXiv:2003.04297 (2020). https:\/\/doi.org\/10.48550\/arXiv.2003.04297","DOI":"10.48550\/arXiv.2003.04297"},{"key":"5231_CR17","doi-asserted-by":"publisher","unstructured":"Radford, A., Kim, J. W., Hallacy, C., et al.: Learning transferable visual models from natural language supervision. In\u00a0International Conference on Machine Learning (ICML), PMLR 139, pp. 8748\u20138763 (2021). https:\/\/doi.org\/10.48550\/arXiv.2103.00020","DOI":"10.48550\/arXiv.2103.00020"},{"key":"5231_CR18","doi-asserted-by":"publisher","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770\u2013778 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"5231_CR19","doi-asserted-by":"publisher","unstructured":"Yuan, L., Chen, Y., Wang, T., et al.: Tokens-to-token ViT: Training vision transformers from scratch on ImageNet. In\u00a0Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 558\u2013567 (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00060","DOI":"10.1109\/ICCV48922.2021.00060"},{"issue":"1","key":"5231_CR20","doi-asserted-by":"publisher","first-page":"012173","DOI":"10.1088\/1742-6596\/1963\/1\/012173","volume":"1963","author":"AH Mohammed","year":"2021","unstructured":"Mohammed, A.H., Ali, A.H.: Survey of BERT (bidirectional encoder representation transformer) types. J. Phys. Conf. Ser. 1963(1), 012173 (2021). https:\/\/doi.org\/10.1088\/1742-6596\/1963\/1\/012173","journal-title":"J. Phys. Conf. Ser."},{"key":"5231_CR21","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2002.07394","author":"J Li","year":"2020","unstructured":"Li, J., Socher, R., Hoi, S.C.H.: DivideMix: learning with noisy labels as semi-supervised learning. arXiv (2020). https:\/\/doi.org\/10.48550\/arXiv.2002.07394","journal-title":"arXiv"},{"key":"5231_CR22","doi-asserted-by":"publisher","unstructured":"Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5552\u20135560 (2018). https:\/\/doi.org\/10.1109\/CVPR.2018.00582","DOI":"10.1109\/CVPR.2018.00582"},{"key":"5231_CR23","doi-asserted-by":"publisher","unstructured":"Li, W., Wang, L., Li, W., et al.: WebVision database: Visual learning and understanding from web data. arXiv:1708.02862 (2017). https:\/\/doi.org\/10.48550\/arXiv.1708.02862","DOI":"10.48550\/arXiv.1708.02862"},{"key":"5231_CR24","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Sabuncu, M. R.: Generalized cross entropy loss for training deep neural networks with noisy labels. Adv. Neural Inf. Process. Syst. 31 (2018). https:\/\/doi.org\/10.48550\/arXiv.1805.07836","DOI":"10.48550\/arXiv.1805.07836"},{"key":"5231_CR25","doi-asserted-by":"publisher","unstructured":"Liu, S., Niles-Weed, J., Razavian, N., et al.: Early-learning regularization prevents memorization of noisy labels. Adv. Neural Inf. Process. Syst. 33, 20331\u201320342 (2020). https:\/\/doi.org\/10.48550\/arXiv.2007.00151","DOI":"10.48550\/arXiv.2007.00151"},{"key":"5231_CR26","first-page":"2579","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05231-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-026-05231-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-026-05231-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T20:32:25Z","timestamp":1776976345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-026-05231-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":26,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["5231"],"URL":"https:\/\/doi.org\/10.1007\/s11760-026-05231-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"15 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 October 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"240"}}