{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:12:01Z","timestamp":1778857921248,"version":"3.51.4"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276281"],"award-info":[{"award-number":["62276281"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2024A1515011882"],"award-info":[{"award-number":["2024A1515011882"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Vision and Applications"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00138-026-01822-z","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T18:53:09Z","timestamp":1777488789000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SSMC: spatial-spectral mask consistency learning for semi-supervised medical image segmentation"],"prefix":"10.1007","volume":"37","author":[{"given":"Yidan","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenyu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianjun","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiong","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andy Jin-Hua","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"1822_CR1","doi-asserted-by":"crossref","unstructured":"Chen, F., Fei, J., Chen, Y., Huang, C.: Decoupled consistency for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 551\u2013561. Springer (2023)","DOI":"10.1007\/978-3-031-43907-0_53"},{"issue":"3","key":"1822_CR2","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1148\/radiol.11091710","volume":"261","author":"B Van Ginneken","year":"2011","unstructured":"Van Ginneken, B., Schaefer-Prokop, C.M., Prokop, M.: Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 261(3), 719\u2013732 (2011)","journal-title":"Radiology"},{"key":"1822_CR3","doi-asserted-by":"crossref","unstructured":"Wu, Y., Xu, M., Ge, Z., Cai, J., Zhang, L.: Semi-supervised left atrium segmentation with mutual consistency training. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 297\u2013306. Springer (2021)","DOI":"10.1007\/978-3-030-87196-3_28"},{"key":"1822_CR4","doi-asserted-by":"publisher","first-page":"104366","DOI":"10.1016\/j.jbi.2023.104366","volume":"142","author":"MA Ribeiro","year":"2023","unstructured":"Ribeiro, M.A., Nunes, F.L.: Left ventricle segmentation combining deep learning and deformable models with anatomical constraints. J. Biomed. Inform. 142, 104366 (2023)","journal-title":"J. Biomed. Inform."},{"issue":"6","key":"1822_CR5","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s00138-024-01619-y","volume":"35","author":"P Paithane","year":"2024","unstructured":"Paithane, P.: Optimize multiscale feature hybrid-net deep learning approach used for automatic pancreas image segmentation. Mach. Vis. Appl. 35(6), 135 (2024)","journal-title":"Mach. Vis. Appl."},{"key":"1822_CR6","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, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., S\u00e1nchez, C.I.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60\u201388 (2017)","journal-title":"Med. Image Anal."},{"key":"1822_CR7","doi-asserted-by":"publisher","first-page":"100261","DOI":"10.1016\/j.health.2023.100261","volume":"4","author":"AE Ilesanmi","year":"2023","unstructured":"Ilesanmi, A.E., Ilesanmi, T., Gbotoso, G.A.: A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks. Healthcare Anal. 4, 100261 (2023)","journal-title":"Healthcare Anal."},{"key":"1822_CR8","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30 (2017)"},{"key":"1822_CR9","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., Ahmadi, S.-A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571. IEEE (2016)","DOI":"10.1109\/3DV.2016.79"},{"key":"1822_CR10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2","key":"1822_CR11","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s00138-024-01513-7","volume":"35","author":"X Zhang","year":"2024","unstructured":"Zhang, X., Xu, G., Wu, X., Liao, W., Leng, X., Wang, X., He, X., Li, C.: A pixel and channel enhanced up-sampling module for biomedical image segmentation. Mach. Vis. Appl. 35(2), 30 (2024)","journal-title":"Mach. Vis. Appl."},{"issue":"5","key":"1822_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-025-01723-7","volume":"36","author":"M He","year":"2025","unstructured":"He, M., Liu, N., Bai, J., Xu, J., Tang, Y., Liu, Y.: Semi-supervised medical image segmentation with joint pseudo supervision. Mach. Vis. Appl. 36(5), 1\u201318 (2025)","journal-title":"Mach. Vis. Appl."},{"key":"1822_CR13","doi-asserted-by":"crossref","unstructured":"Bai, Y., Chen, D., Li, Q., Shen, W., Wang, Y.: Bidirectional copy-paste for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11514\u201311524 (2023)","DOI":"10.1109\/CVPR52729.2023.01108"},{"key":"1822_CR14","doi-asserted-by":"crossref","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. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 605\u2013613. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"1822_CR15","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"},{"issue":"11","key":"1822_CR16","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":"1822_CR17","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"1822_CR18","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"1822_CR19","unstructured":"Bao, H., Dong, L., Piao, S., Wei, F.: Beit: Bert pre-training of image transformers. arXiv preprint arXiv:2106.08254 (2021)"},{"key":"1822_CR20","doi-asserted-by":"crossref","unstructured":"Hoyer, L., Dai, D., Wang, H., Van\u00a0Gool, L.: Mic: Masked image consistency for context-enhanced domain adaptation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11721\u201311732 (2023)","DOI":"10.1109\/CVPR52729.2023.01128"},{"key":"1822_CR21","doi-asserted-by":"crossref","unstructured":"Xie, Z., Zhang, Z., Cao, Y., Lin, Y., Bao, J., Yao, Z., Dai, Q., Hu, H.: Simmim: a simple framework for masked image modeling. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653\u20139663 (2022)","DOI":"10.1109\/CVPR52688.2022.00943"},{"key":"1822_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"1822_CR23","doi-asserted-by":"crossref","unstructured":"Kakogeorgiou, I., Gidaris, S., Psomas, B., Avrithis, Y., Bursuc, A., Karantzalos, K., Komodakis, N.: What to hide from your students: attention-guided masked image modeling. In: European Conference on Computer Vision, pp. 300\u2013318. Springer (2022)","DOI":"10.1007\/978-3-031-20056-4_18"},{"key":"1822_CR24","doi-asserted-by":"publisher","first-page":"102880","DOI":"10.1016\/j.media.2023.102880","volume":"88","author":"Z Xu","year":"2023","unstructured":"Xu, Z., Wang, Y., Lu, D., Luo, X., Yan, J., Zheng, Y., Tong, R.K.-Y.: Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation. Med. Image Anal. 88, 102880 (2023)","journal-title":"Med. Image Anal."},{"key":"1822_CR25","doi-asserted-by":"publisher","first-page":"107398","DOI":"10.1016\/j.compbiomed.2023.107398","volume":"165","author":"W Li","year":"2023","unstructured":"Li, W., Lu, W., Chu, J., Tian, Q., Fan, F.: Confidence-guided mask learning for semi-supervised medical image segmentation. Comput. Biol. Med. 165, 107398 (2023)","journal-title":"Comput. Biol. Med."},{"issue":"22","key":"1822_CR26","doi-asserted-by":"publisher","first-page":"26797","DOI":"10.1007\/s10489-023-04950-5","volume":"53","author":"JH Park","year":"2023","unstructured":"Park, J.H., Kim, J.H., Ngo, B.H., Kwon, J.E., Cho, S.I.: Adversarial representation teaching with perturbation-agnostic student-teacher structure for semi-supervised learning. Appl. Intell. 53(22), 26797\u201326809 (2023)","journal-title":"Appl. Intell."},{"key":"1822_CR27","doi-asserted-by":"publisher","first-page":"113771","DOI":"10.1016\/j.asoc.2025.113771","volume":"184","author":"BH Ngo","year":"2025","unstructured":"Ngo, B.H., Choi, T.J.: Cross-domain knowledge distillation for domain adaptation with GCN-driven MLP generalization. Appl. Soft Comput. 184, 113771 (2025)","journal-title":"Appl. Soft Comput."},{"key":"1822_CR28","doi-asserted-by":"publisher","first-page":"91137","DOI":"10.1109\/ACCESS.2022.3202190","volume":"10","author":"SJ Park","year":"2022","unstructured":"Park, S.J., Park, H.J., Kang, E.S., Ngo, B.H., Lee, H.S., Cho, S.I.: Pseudo label rectification via co-teaching and decoupling for multisource domain adaptation in semantic segmentation. IEEE Access 10, 91137\u201391149 (2022)","journal-title":"IEEE Access"},{"key":"1822_CR29","unstructured":"Bizeul, A., Sutter, T., Ryser, A., Sch\u00f6lkopf, B., K\u00fcgelgen, J., Vogt, J.E.: From pixels to components: eigenvector masking for visual representation learning. arXiv preprint arXiv:2502.06314 (2025)"},{"key":"1822_CR30","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhang, X.: Understanding masked image modeling via learning occlusion invariant feature. arXiv preprint arXiv:2208.04164 (2022)","DOI":"10.1109\/CVPR52729.2023.00604"},{"key":"1822_CR31","doi-asserted-by":"crossref","unstructured":"Nussbaumer, H.J.: The fast Fourier transform. In: Fast Fourier Transform and Convolution Algorithms, pp. 80\u2013111. Springer (1981)","DOI":"10.1007\/978-3-662-00551-4_4"},{"key":"1822_CR32","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fnint.2014.00037","volume":"8","author":"L Kauffmann","year":"2014","unstructured":"Kauffmann, L., Ramano\u00ebl, S., Peyrin, C.: The neural bases of spatial frequency processing during scene perception. Front. Integr. Neurosci. 8, 37 (2014)","journal-title":"Front. Integr. Neurosci."},{"key":"1822_CR33","doi-asserted-by":"crossref","unstructured":"Wang, W., Wang, J., Chen, C., Jiao, J., Cai, Y., Song, S., Li, J.: Fremim: Fourier transform meets masked image modeling for medical image segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 7860\u20137870 (2024)","DOI":"10.1109\/WACV57701.2024.00768"},{"issue":"2","key":"1822_CR34","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1109\/TMI.2024.3469214","volume":"44","author":"X You","year":"2025","unstructured":"You, X., He, J., Yang, J., Gu, Y.: Learning with explicit shape priors for medical image segmentation. IEEE Trans. Med. Imaging 44(2), 927\u2013940 (2025)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"1822_CR35","doi-asserted-by":"crossref","unstructured":"Chen, Y., Fan, H., Xu, B., Yan, Z., Kalantidis, Y., Rohrbach, M., Yan, S., Feng, J.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3435\u20133444 (2019)","DOI":"10.1109\/ICCV.2019.00353"},{"key":"1822_CR36","doi-asserted-by":"publisher","first-page":"23495","DOI":"10.52202\/068431-1707","volume":"35","author":"C Si","year":"2022","unstructured":"Si, C., Yu, W., Zhou, P., Zhou, Y., Wang, X., Yan, S.: Inception transformer. Adv. Neural. Inf. Process. Syst. 35, 23495\u201323509 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"5","key":"1822_CR37","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1109\/PROC.1981.12022","volume":"69","author":"AV Oppenheim","year":"2005","unstructured":"Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69(5), 529\u2013541 (2005)","journal-title":"Proc. IEEE"},{"key":"1822_CR38","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: European Conference on Computer Vision, pp. 694\u2013711. Springer (2016)","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"1822_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"1822_CR40","unstructured":"Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144 (2016)"},{"key":"1822_CR41","unstructured":"Luo, X.: SSL4MIS. https:\/\/github.com\/HiLab-git\/SSL4MIS (2020)"},{"issue":"2","key":"1822_CR42","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","volume":"18","author":"G Litjens","year":"2014","unstructured":"Litjens, G., Toth, R., Van De Ven, W., Hoeks, C., Kerkstra, S., Van Ginneken, B., Vincent, G., Guillard, G., Birbeck, N., Zhang, J., et al.: Evaluation of prostate segmentation algorithms for MRI: the promise12 challenge. Med. Image Anal. 18(2), 359\u2013373 (2014)","journal-title":"Med. Image Anal."},{"key":"1822_CR43","doi-asserted-by":"crossref","unstructured":"Liu, J., Desrosiers, C., Zhou, Y.: Semi-supervised medical image segmentation using cross-model pseudo-supervision with shape awareness and local context constraints. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 140\u2013150 (2022)","DOI":"10.1007\/978-3-031-16452-1_14"},{"key":"1822_CR44","unstructured":"Spyridon, B., Mauricio, R., Andras, J.E.A.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629 (2019)"},{"key":"1822_CR45","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."},{"key":"1822_CR46","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, pp. 12674\u201312684 (2020)","DOI":"10.1109\/CVPR42600.2020.01269"},{"key":"1822_CR47","doi-asserted-by":"crossref","unstructured":"Luo, X., Liao, W., Chen, J., Song, T., Chen, Y., Zhang, S., Chen, N., Wang, G., Zhang, S.: Efficient semi-supervised gross target volume of nasopharyngeal carcinoma segmentation via uncertainty rectified pyramid consistency. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 318\u2013329 (2021)","DOI":"10.1007\/978-3-030-87196-3_30"},{"key":"1822_CR48","doi-asserted-by":"publisher","first-page":"102530","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.: Mutual consistency learning for semi-supervised medical image segmentation. Med. Image Anal. 81, 102530 (2022)","journal-title":"Med. Image Anal."},{"key":"1822_CR49","doi-asserted-by":"crossref","unstructured":"Basak, H., Yin, Z.: Pseudo-label guided contrastive learning for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19786\u201319797 (2023)","DOI":"10.1109\/CVPR52729.2023.01895"},{"key":"1822_CR50","unstructured":"Zhu, Y., Yang, J., Liu, S., Zhang, R.: Inherent consistent learning for accurate semi-supervised medical image segmentation. In: Medical Imaging with Deep Learning, pp. 1581\u20131601 (2023)"},{"key":"1822_CR51","doi-asserted-by":"crossref","unstructured":"Chi, H., Pang, J., Zhang, B., Liu, W.: Adaptive bidirectional displacement for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4070\u20134080 (2024)","DOI":"10.1109\/CVPR52733.2024.00390"},{"key":"1822_CR52","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Z., Yue, J., Guo, W., Li, D.: M3hl: Mutual mask mix with high-low level feature consistency for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 314\u2013323. Springer (2025)","DOI":"10.1007\/978-3-032-04937-7_30"},{"key":"1822_CR53","doi-asserted-by":"crossref","unstructured":"Wu, Y., Wu, Z., Wu, Q., Ge, Z., Cai, J.: Exploring smoothness and class-separation for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 13435, pp. 34\u201343. Springer, Cham (2022)","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"1822_CR54","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Wang, Z., Wang, L., Yu, D., Yuan, Y., Zhou, L.: Alternate diverse teaching for semi-supervised medical image segmentation. In: European Conference on Computer Vision, pp. 227\u2013243. Springer (2024)","DOI":"10.1007\/978-3-031-72652-1_14"}],"container-title":["Machine Vision and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-026-01822-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00138-026-01822-z","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00138-026-01822-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T14:29:23Z","timestamp":1778855363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00138-026-01822-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,29]]},"references-count":54,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["1822"],"URL":"https:\/\/doi.org\/10.1007\/s00138-026-01822-z","relation":{},"ISSN":["0932-8092","1432-1769"],"issn-type":[{"value":"0932-8092","type":"print"},{"value":"1432-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,29]]},"assertion":[{"value":"24 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"63"}}