{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T07:08:17Z","timestamp":1780988897846,"version":"3.54.1"},"reference-count":84,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Open Project Program of Guangxi Key Laboratory of Digital Infrastructure","award":["No. GXDINBC202401"],"award-info":[{"award-number":["No. GXDINBC202401"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["No. 62266004"],"award-info":[{"award-number":["No. 62266004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-025-00786-8","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T01:33:49Z","timestamp":1743557629000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Better Pseudo-Labeling for Semi-Supervised Domain Generalization in Medical Magnetic Resonance Image Segmentation"],"prefix":"10.1007","volume":"18","author":[{"given":"Liangqing","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zuqiang","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaohong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yumin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"key":"786_CR1","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.media.2014.09.005","volume":"19","author":"W Bai","year":"2015","unstructured":"Bai, W., Shi, W., Ledig, C., Rueckert, D.: Multi-atlas segmentation with augmented features for cardiac MR images. Med. Image Anal. 19, 98\u2013109 (2015)","journal-title":"Med. Image Anal."},{"key":"786_CR2","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.media.2017.10.001","volume":"43","author":"X Alba","year":"2018","unstructured":"Alba, X., Lekadir, K., Pereanez, M., Medrano-Gracia, P., Young, A.A., Frangi, A.F.: Automatic initialization and quality control of large-scale cardiac MRI segmentations. Med. Image Anal. 43, 129\u2013141 (2018)","journal-title":"Med. Image Anal."},{"key":"786_CR3","unstructured":"Tran P.V.: A fully convolutional neural network for cardiac segmentation in short-axis MRI. arXiv preprint arXiv:1604.00494 (2017)"},{"key":"786_CR4","first-page":"234","volume":"18","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. Proc. Med. Image Comput. Comput. Assist. Interv. 18, 234\u2013241 (2015)","journal-title":"Proc. Med. Image Comput. Comput. Assist. Interv."},{"issue":"12","key":"786_CR5","doi-asserted-by":"publisher","first-page":"3543","DOI":"10.1109\/TMI.2021.3090082","volume":"40","author":"VM Campello","year":"2021","unstructured":"Campello, V.M., Gkontra, P., Izquierdo, C., Martin-Isla, C., Sojoudi, A., Full, P.M., Lekadir, K.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge. IEEE Trans. Med. Imaging 40(12), 3543\u20133554 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"786_CR6","unstructured":"Isensee, F., J\u00e4ger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: Automated design of deep learning methods for biomedical image segmentation. arXiv preprint arXiv:1904.08128 (2019)"},{"key":"786_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101693","volume":"63","author":"N Tajbakhsh","year":"2020","unstructured":"Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J.N., Wu, Z., Ding, X.: Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation. Med. Image Anal. 63, 101693 (2020)","journal-title":"Med. Image Anal."},{"key":"786_CR8","doi-asserted-by":"publisher","first-page":"102088","DOI":"10.1016\/j.compmedimag.2022.102088","volume":"99","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Liao, Q., Ding, L., Zhang, J.: Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: an empirical study of 2.5 D solutions. Comput. Med. Imaging Graph. 99, 102088 (2022)","journal-title":"Comput. Med. Imaging Graph."},{"issue":"7","key":"786_CR9","doi-asserted-by":"publisher","first-page":"2531","DOI":"10.1109\/TMI.2020.2973595","volume":"39","author":"L Zhang","year":"2020","unstructured":"Zhang, L., Wang, X., Yang, D., Sanford, T., Harmon, S., Turkbey, B., Xu, Z.: Generalizing deep learning for medical image segmentation to unseen domains via deep stacked transformation. IEEE Trans. Med. Imaging 39(7), 2531\u20132540 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"786_CR10","doi-asserted-by":"publisher","first-page":"2808","DOI":"10.1109\/TMI.2021.3066161","volume":"40","author":"Q Yao","year":"2021","unstructured":"Yao, Q., Xiao, L., Liu, P., Zhou, S.K.: Label-free segmentation of COVID-19 lesions in lung CT. IEEE Trans. Med. Imaging 40(10), 2808\u20132819 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"786_CR11","doi-asserted-by":"publisher","first-page":"4152","DOI":"10.1109\/JBHI.2021.3106341","volume":"25","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Liao, Q., Yuan, L., Zhu, H., Xing, J., Zhang, J.: Exploiting shared knowledge from non-COVID lesions for annotation-efficient COVID-19 CT lung infection segmentation. IEEE J. Biomed. Health Inform. 25(11), 4152\u20134162 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"786_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107840","volume":"169","author":"R Jiao","year":"2024","unstructured":"Jiao, R., Zhang, Y., Ding, L., Xue, B., Zhang, J., Cai, R., Jin, C.: Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation. Comput. Biol. Med. 169, 107840 (2024)","journal-title":"Comput. Biol. Med."},{"key":"786_CR13","unstructured":"Lee, D.H.: Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Proceedings of the Workshop on Challenges in Representation Learning, ICML, vol. 3(2), p. 896 (2013)"},{"issue":"8","key":"786_CR14","doi-asserted-by":"publisher","first-page":"10427","DOI":"10.1109\/TPAMI.2023.3240886","volume":"45","author":"LL Zeng","year":"2023","unstructured":"Zeng, L.L., Gao, K., Hu, D., Feng, Z., Hou, C., Rong, P., Wang, W.: SS-TBN: a semi-supervised tri-branch network for COVID-19 screening and lesion segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8), 10427\u201310442 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"786_CR15","doi-asserted-by":"publisher","first-page":"102459","DOI":"10.1016\/j.media.2022.102459","volume":"79","author":"X Wang","year":"2022","unstructured":"Wang, X., Yuan, Y., Guo, D., Huang, X., Cui, Y., Xia, M., Chen, S.: SSA-Net: spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning. Med. Image Anal. 79, 102459 (2022)","journal-title":"Med. Image Anal."},{"issue":"3","key":"786_CR16","first-page":"3099","volume":"36","author":"H Yao","year":"2022","unstructured":"Yao, H., Hu, X., Li, X.: Enhancing pseudo label quality for semi-supervised domain-generalized medical image segmentation. Proc. AAAI Conf. Artif. Intell. 36(3), 3099\u20133107 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"786_CR17","unstructured":"Zou, Y., Zhang, Z., Zhang, H., Li, C.L., Bian, X., Huang, J.B., Pfister, T.: Pseudoseg: designing pseudo labels for semantic segmentation. arXiv preprint arXiv:2010.09713 (2020)"},{"key":"786_CR18","doi-asserted-by":"crossref","unstructured":"You, C., Zhao, R., Staib, L.H., Duncan, J.S.: Momentum contrastive voxel-wise representation learning for semi-supervised volumetric medical image segmentation. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 639\u2013652 (2022)","DOI":"10.1007\/978-3-031-16440-8_61"},{"issue":"9","key":"786_CR19","doi-asserted-by":"publisher","first-page":"2228","DOI":"10.1109\/TMI.2022.3161829","volume":"41","author":"C You","year":"2022","unstructured":"You, C., Zhou, Y., Zhao, R., Staib, L., Duncan, J.S.: Simcvd: simple contrastive voxel-wise representation distillation for semi-supervised medical image segmentation. IEEE Trans. Med. Imaging 41(9), 2228\u20132237 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"786_CR20","unstructured":"You, C., Dai, W., Min, Y., Liu, F., Clifton, D., Zhou, S.K., Duncan, J.: Rethinking semi-supervised medical image segmentation: a variance-reduction perspective. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"786_CR21","doi-asserted-by":"crossref","unstructured":"You, C., Xiang, J., Su, K., Zhang, X., Dong, S., Onofrey, J., Duncan, J.S.: Incremental learning meets transfer learning: application to multi-site prostate mri segmentation. In: International Workshop on Distributed, Collaborative, and Federated Learning, pp. 3\u201316 (2022)","DOI":"10.1007\/978-3-031-18523-6_1"},{"key":"786_CR22","doi-asserted-by":"crossref","unstructured":"You, C., Yang, J., Chapiro, J., & Duncan, J.S.: Unsupervised wasserstein distance guided domain adaptation for 3D multi-domain liver segmentation. In: International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, vol. 3, pp. 155\u2013163 (2020)","DOI":"10.1007\/978-3-030-61166-8_17"},{"key":"786_CR23","first-page":"29582","volume":"35","author":"C You","year":"2022","unstructured":"You, C., Zhao, R., Liu, F., Dong, S., Chinchali, S., Topcu, U., Duncan, J.: Class-aware adversarial transformers for medical image segmentation. Adv. Neural. Inf. Process. Syst. 35, 29582\u201329596 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"786_CR24","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Min, Y., Staib, L., Duncan, J.S.: Bootstrapping semi-supervised medical image segmentation with anatomical-aware contrastive distillation. In: International Conference on Medical Imaging, pp. 641\u2013653 (2023)","DOI":"10.1007\/978-3-031-34048-2_49"},{"key":"786_CR25","doi-asserted-by":"publisher","first-page":"11136","DOI":"10.1109\/TPAMI.2024.3461321","volume":"13","author":"C You","year":"2024","unstructured":"You, C., Dai, W., Liu, F., Min, Y., Dvornek, N.C., Li, X., Duncan, J.S.: Mine your own anatomy: revisiting medical image segmentation with extremely limited labels. IEEE Trans. Pattern Anal. Mach. Intell. 13, 11136\u201311151 (2024)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"786_CR26","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Min, Y., Staib, L., Sekhon, J., Duncan, J.S.: Action++: improving semi-supervised medical image segmentation with adaptive anatomical contrast. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 194\u2013205 (2023)","DOI":"10.1007\/978-3-031-43901-8_19"},{"key":"786_CR27","doi-asserted-by":"crossref","unstructured":"You, C., Dai, W., Min, Y., Staib, L., Duncan, J. S.: Implicit anatomical rendering for medical image segmentation with stochastic experts. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 561\u2013571 (2023)","DOI":"10.1007\/978-3-031-43898-1_54"},{"key":"786_CR28","unstructured":"Oliver, A., Odena, A., Raffel, C. A., Cubuk, E. D., Goodfellow, I.: Realistic evaluation of deep semi-supervised learning algorithms. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"issue":"11","key":"786_CR29","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., Lin, F., Zhang, K., Feng, Z., Heng, P.A.: Self-ensembling co-training framework for semi-supervised COVID-19 CT segmentation. IEEE J. Biomed. Health Inform. 25(11), 4140\u20134151 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"3","key":"786_CR30","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/TMI.2021.3117888","volume":"41","author":"Y Shi","year":"2021","unstructured":"Shi, Y., Zhang, J., Ling, T., Lu, J., Zheng, Y., Yu, Q., Gao, Y.: Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation. IEEE Trans. Med. Imaging 41(3), 608\u2013620 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"786_CR31","doi-asserted-by":"crossref","unstructured":"Li, N., Xiong, L., Qiu, W., Pan, Y., Luo, Y., Zhang, Y.: Segment anything model for semi-supervised medical image segmentation via selecting reliable pseudo-labels. In: Proceedings of the International Conference on Neural Information Processing, pp. 138\u2013149 (2023)","DOI":"10.1007\/978-981-99-8141-0_11"},{"issue":"8","key":"786_CR32","doi-asserted-by":"publisher","first-page":"3999","DOI":"10.1109\/JBHI.2022.3167384","volume":"26","author":"K Han","year":"2022","unstructured":"Han, K., Liu, L., Song, Y., Liu, Y., Qiu, C., Tang, Y., Liu, Z.: An effective semi-supervised approach for liver CT image segmentation. IEEE J. Biomed. Health Inform. 26(8), 3999\u20134007 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"786_CR33","doi-asserted-by":"crossref","unstructured":"Wang, R., Wu, Y., Chen, H., Wang, L., Meng, D.: Neighbor matching for semi-supervised learning. In: Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention, pp. 439\u2013449 (2021)","DOI":"10.1007\/978-3-030-87196-3_41"},{"issue":"2","key":"786_CR34","first-page":"2171","volume":"36","author":"CM Seibold","year":"2022","unstructured":"Seibold, C.M., Rei\u00df, S., Kleesiek, J., Stiefelhagen, R.: Reference-guided pseudo-label generation for medical semantic segmentation. Proc. AAAI Conf. Artif. Intell. 36(2), 2171\u20132179 (2022)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"786_CR35","first-page":"605","volume":"22","author":"L Yu","year":"2019","unstructured":"Yu, L., Wang, S., Li, X., Fu, C.W., Heng, P.A.: Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation. Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv. 22, 605\u2013613 (2019)","journal-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv."},{"key":"786_CR36","doi-asserted-by":"publisher","first-page":"101766","DOI":"10.1016\/j.media.2020.101766","volume":"65","author":"Y Xia","year":"2020","unstructured":"Xia, Y., Yang, D., Yu, Z., Liu, F., Cai, J., Yu, L., Roth, H.: Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation. Med. Image Anal. 65, 101766 (2020)","journal-title":"Med. Image Anal."},{"key":"786_CR37","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Advances in neural information processing systems, vol. 29 (2016)"},{"key":"786_CR38","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)"},{"key":"786_CR39","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, vol. 30 (2017)"},{"key":"786_CR40","doi-asserted-by":"crossref","unstructured":"Alonso, I., Sabater, A., Ferstl, D., Montesano, L., Murillo, A.C.: Semi-supervised semantic segmentation with pixel-level contrastive learning from a class-wise memory bank. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8219\u20138228 (2021)","DOI":"10.1109\/ICCV48922.2021.00811"},{"key":"786_CR41","doi-asserted-by":"crossref","unstructured":"Kwon, D., Kwak, S.: Semi-supervised semantic segmentation with error localization network. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9957\u20139967 (2022)","DOI":"10.1109\/CVPR52688.2022.00972"},{"key":"786_CR42","doi-asserted-by":"crossref","unstructured":"Liu, Y., Tian, Y., Chen, Y., Liu, F., Belagiannis, V., Carneiro, G.: Perturbed and strict mean teachers for semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Computer Vision and Pattern Recognition, pp. 4258\u20134267 (2022)","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"786_CR43","unstructured":"Liu, S., Zhi, S., Johns, E., Davison, A.J.: Bootstrapping semantic segmentation with regional contrast. arXiv preprint arXiv:2104.04465 (2021)"},{"issue":"4","key":"786_CR44","doi-asserted-by":"publisher","first-page":"1369","DOI":"10.1109\/TPAMI.2019.2960224","volume":"43","author":"S Mittal","year":"2019","unstructured":"Mittal, S., Tatarchenko, M., Brox, T.: Semi-supervised semantic segmentation with high-and low-level consistency. IEEE Trans. Pattern Anal. 43(4), 1369\u20131379 (2019)","journal-title":"IEEE Trans. Pattern Anal."},{"key":"786_CR45","unstructured":"DeVries, T.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"786_CR46","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"issue":"11","key":"786_CR47","doi-asserted-by":"publisher","first-page":"3016","DOI":"10.1109\/TMI.2022.3176050","volume":"41","author":"W Huang","year":"2022","unstructured":"Huang, W., Chen, C., Xiong, Z., Zhang, Y., Chen, X., Sun, X., Wu, F.: Semi-supervised neuron segmentation via reinforced consistency learning. IEEE Trans. Med. Imaging 41(11), 3016\u20133028 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"786_CR48","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. In: Advances in neural information processing systems, vol. 32 (2019)"},{"key":"786_CR49","unstructured":"Berthelot, D., Carlini, N., Cubuk, E.D., Kurakin, A., Sohn, K., Zhang, H., Raffel, C.: Remixmatch: semi-supervised learning with distribution alignment and augmentation anchoring. arXiv preprint arXiv:1911.09785 (2019)"},{"key":"786_CR50","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1109\/TMM.2022.3154159","volume":"25","author":"Y Shu","year":"2022","unstructured":"Shu, Y., Li, H., Xiao, B., Bi, X., Li, W.: Cross-mix monitoring for medical image segmentation with limited supervision. IEEE Trans. Multimed. 25, 1700\u20131712 (2022)","journal-title":"IEEE Trans. Multimed."},{"key":"786_CR51","doi-asserted-by":"crossref","unstructured":"Basak, H., Bhattacharya, R., Hussain, R., & Chatterjee, A.: An exceedingly simple consistency regularization method for semi-supervised medical image segmentation. In: 2022 IEEE 19th International Symposium on Biomedical Imaging, pp. 1\u20134 (2022)","DOI":"10.1109\/ISBI52829.2022.9761602"},{"key":"786_CR52","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE\/CVF C Conference on Computer Vision and Pattern Recognition, pp, 10687\u201310698 (2020)","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"786_CR53","first-page":"199","volume":"24","author":"Y Li","year":"2021","unstructured":"Li, Y., Luo, L., Lin, H., Chen, H., Heng, P.A.: Dual-consistency semi-supervised learning with uncertainty quantification for COVID-19 lesion segmentation from CT images. Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv. 24, 199\u2013209 (2021)","journal-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Interv."},{"key":"786_CR54","first-page":"5264","volume":"34","author":"AT Nguyen","year":"2021","unstructured":"Nguyen, A.T., Tran, T., Gal, Y., Baydin, A.G.: Domain invariant representation learning with domain density transformations. Adv. Neural. Inf. Process. Syst. 34, 5264\u20135275 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"786_CR55","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1109\/TMM.2023.3263549","volume":"26","author":"Z Niu","year":"2024","unstructured":"Niu, Z., Yuan, J., Ma, X., Xu, Y., Liu, J., Chen, Y.W., Lin, L.: Knowledge distillation-based domain-invariant representation learning for domain generalization. IEEE Trans. Multimed. 26, 245\u2013255 (2024)","journal-title":"IEEE Trans. Multimed."},{"key":"786_CR56","first-page":"21189","volume":"34","author":"MH Bui","year":"2021","unstructured":"Bui, M.H., Tran, T., Tran, A., Phung, D.: Exploiting domain-specific features to enhance domain generalization. Adv. Neural. Inf. Process. Syst. 34, 21189\u201321201 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"786_CR57","first-page":"3490","volume":"32","author":"D Li","year":"2018","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: meta-learning for domain generalization. Proc. AAAI Conf. Artif. Intell. 32(1), 3490\u20133497 (2018)","journal-title":"AAAI Conf. Artif. Intell."},{"key":"786_CR58","doi-asserted-by":"crossref","unstructured":"Shu, Y., Cao, Z., Wang, C., Wang, J., Long, M.: Open domain generalization with domain-augmented meta-learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9624\u20139633 (2021)","DOI":"10.1109\/CVPR46437.2021.00950"},{"key":"786_CR59","doi-asserted-by":"crossref","unstructured":"Qiao, F., Zhao, L., Peng, X.: Learning to learn single domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12556\u201312565 (2020)","DOI":"10.1109\/CVPR42600.2020.01257"},{"key":"786_CR60","unstructured":"Xu, Z., Liu, D., Yang, J., Raffel, C., Niethammer, M.: Robust and generalizable visual representation learning via random convolutions. arXiv preprint arXiv:2007.13003 (2020)"},{"issue":"9","key":"786_CR61","doi-asserted-by":"publisher","first-page":"2377","DOI":"10.1007\/s11263-023-01821-x","volume":"131","author":"K Zhou","year":"2023","unstructured":"Zhou, K., Loy, C.C., Liu, Z.: Semi-supervised domain generalization with stochastic stylematch. Int. J. Comput. Vis. 131(9), 2377\u20132387 (2023)","journal-title":"Int. J. Comput. Vis."},{"key":"786_CR62","doi-asserted-by":"crossref","unstructured":"Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1501\u20131510 (2017)","DOI":"10.1109\/ICCV.2017.167"},{"issue":"3","key":"786_CR63","doi-asserted-by":"publisher","first-page":"822","DOI":"10.1007\/s11263-023-01913-8","volume":"132","author":"K Zhou","year":"2024","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Mixstyle neural networks for domain generalization and adaptation. Int. J. Comput. Vis. 132(3), 822\u2013836 (2024)","journal-title":"Int. J. Comput. Vis."},{"key":"786_CR64","first-page":"124","volume":"16","author":"Z Huang","year":"2020","unstructured":"Huang, Z., Wang, H., Xing, E.P., Huang, D.: Self-challenging improves cross-domain generalization. Proc. Eur. Conf. Comput. Vis. 16, 124\u2013140 (2020)","journal-title":"Proc. Eur. Conf. Comput. Vis."},{"key":"786_CR65","first-page":"429","volume":"16","author":"Z Ke","year":"2020","unstructured":"Ke, Z., Qiu, D., Li, K., Yan, Q., Lau, R.W.: Guided collaborative training for pixel-wise semi-supervised learning. Proc. Eur. Conf. Comput. Vis. 16, 429\u2013445 (2020)","journal-title":"Proc. Eur. Conf. Comput. Vis."},{"key":"786_CR66","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C.A., Li, C.L.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596\u2013608 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"786_CR67","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"786_CR68","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, pp. 2613\u20132622 (2021)","DOI":"10.1109\/CVPR46437.2021.00264"},{"key":"786_CR69","unstructured":"French, G., Laine, S., Aila, T., Mackiewicz, M., Finlayson, G.: Semi-supervised semantic segmentation needs strong, varied perturbations. arXiv preprint arXiv:1906.01916 (2019)"},{"key":"786_CR70","first-page":"307","volume":"24","author":"X Liu","year":"2021","unstructured":"Liu, X., Thermos, S., O\u2019Neil, A., Tsaftaris, S.A.: Semi-supervised meta-learning with disentanglement for domain-generalised medical image segmentation. Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent. 24, 307\u2013317 (2021)","journal-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent."},{"key":"786_CR71","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the International Conference on Machine Learning, pp. 1321\u20131330 (2017)"},{"key":"786_CR72","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the International Conference on Machine Learning, pp. 1050\u20131059 (2016)"},{"key":"786_CR73","doi-asserted-by":"crossref","unstructured":"Pham, H., Dai, Z., Xie, Q., Le, Q.V.: Meta pseudo labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11557\u201311568 (2021)","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"786_CR74","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.neuroimage.2017.03.010","volume":"152","author":"F Prados","year":"2017","unstructured":"Prados, F., Ashburner, J., Blaiotta, C., Brosch, T., Carballido-Gamio, J., Cardoso, M.J., Cohen-Adad, J.: Spinal cord grey matter segmentation challenge. Neuroimage 152, 312\u2013329 (2017)","journal-title":"Neuroimage"},{"key":"786_CR75","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110520","volume":"270","author":"J Hu","year":"2023","unstructured":"Hu, J., Gu, X., Wang, Z., Gu, X.: Mixture of calibrated networks for domain generalization in brain tumor segmentation. Knowl. Based Syst. 270, 110520 (2023)","journal-title":"Knowl. Based Syst."},{"key":"786_CR76","doi-asserted-by":"publisher","first-page":"102517","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.: Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Med. Image Anal. 80, 102517 (2022)","journal-title":"Med. Image Anal."},{"key":"786_CR77","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision, pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"786_CR78","first-page":"424","volume":"19","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent. 19, 424\u2013432 (2016)","journal-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent."},{"key":"786_CR79","unstructured":"Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Rueckert, D.: Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)"},{"key":"786_CR80","unstructured":"Chen, H., Dou, Q., Yu, L., Heng, P.A.: Voxresnet: deep voxelwise residual networks for volumetric brain segmentation. arXiv preprint arXiv:1608.05895 (2016)"},{"key":"786_CR81","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: Proceedings of the International Conference on 3D Vision, pp. 565\u2013571 (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"786_CR82","doi-asserted-by":"crossref","unstructured":"Vu, T.H., Jain, H., Bucher, M., Cord, M., P\u00e9rez, P.: Advent: adversarial entropy minimization for domain adaptation in semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"786_CR83","first-page":"408","volume":"20","author":"Y Zhang","year":"2017","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. Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent. 20, 408\u2013416 (2017)","journal-title":"Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent."},{"key":"786_CR84","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma, V., Kawaguchi, K., Lamb, A., Kannala, J., Solin, A., Bengio, Y., Lopez-Paz, D.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90\u2013106 (2022)","journal-title":"Neural Netw."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00786-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-025-00786-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-025-00786-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T20:58:31Z","timestamp":1743713911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-025-00786-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":84,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["786"],"URL":"https:\/\/doi.org\/10.1007\/s44196-025-00786-8","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,31]]},"assertion":[{"value":"6 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2025","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 have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}},{"value":"The data used in this study are derived from publicly accessible datasets and the images in the datasets are completely unrecognizable and no details of the individuals are reported in the manuscript, therefore informed consent is not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and informed consent for data used"}}],"article-number":"71"}}