{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T17:35:56Z","timestamp":1784568956960,"version":"3.55.0"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049360","type":"print"},{"value":"9783032049377","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"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":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-04937-7_30","type":"book-chapter","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:39Z","timestamp":1758260439000},"page":"314-323","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["M$$^{3}$$HL: Mutual Mask Mix with\u00a0High-Low Level Feature Consistency for\u00a0Semi-supervised Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Yajun","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zenghui","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiang","family":"Yue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiwei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongying","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"30_CR1","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 (CVPR), pp. 11514\u201311524 (2023)","DOI":"10.1109\/CVPR52729.2023.01108"},{"issue":"11","key":"30_CR2","doi-asserted-by":"publisher","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","volume":"37","author":"O Bernard","year":"2018","unstructured":"Bernard, O., 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":"30_CR3","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":"30_CR4","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":"30_CR5","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/978-3-031-72111-3_46","volume-title":"MICCAI 2024","author":"J He","year":"2024","unstructured":"He, J., Cai, C., Li, Q., Ma, A.J.: Pair shuffle consistency for semi-supervised medical image segmentation. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15008, pp. 489\u2013499. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72111-3_46"},{"key":"30_CR6","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"},{"issue":"5","key":"30_CR7","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1109\/TMI.2022.3225687","volume":"42","author":"T Lei","year":"2022","unstructured":"Lei, T., Zhang, D., Du, X., Wang, X., Wan, Y., Nandi, A.K.: Semi-supervised medical image segmentation using adversarial consistency learning and dynamic convolution network. IEEE Trans. Med. Imaging 42(5), 1265\u20131277 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"30_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1007\/978-3-030-59710-8_54","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Li","year":"2020","unstructured":"Li, S., Zhang, C., He, X.: Shape-aware semi-supervised 3D semantic segmentation for medical images. In: Martel, A.L., et al. (eds.) MICCAI 2020, Part I. LNCS, vol. 12261, pp. 552\u2013561. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59710-8_54"},{"key":"30_CR9","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/978-3-031-72117-5_55","volume-title":"MICCAI 2024","author":"J Liu","year":"2024","unstructured":"Liu, J., Qian, W., Cao, J., Liu, P.: Overlay mantle-free for semi-supervised medical image segmentation. In: Linguraru, M.G., et al. (eds.) MICCAI 2024. LNCS, vol. 15010, pp. 589\u2013598. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72117-5_55"},{"key":"30_CR10","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 Conference on Computer Vision and Pattern Recognition, pp. 4258\u20134267 (2022)","DOI":"10.1109\/CVPR52688.2022.00422"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Luo, X., Chen, J., Song, T., Wang, G.: Semi-supervised medical image segmentation through dual-task consistency. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a035, pp. 8801\u20138809 (2021)","DOI":"10.1609\/aaai.v35i10.17066"},{"key":"30_CR12","unstructured":"Luo, X., Hu, M., Song, T., Wang, G., Zhang, S.: Semi-supervised medical image segmentation via cross teaching between CNN and transformer. In: Medical Imaging with Deep Learning (2021)"},{"key":"30_CR13","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":"30_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"30_CR15","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":"30_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102530","volume":"81","author":"Y Wu","year":"2022","unstructured":"Wu, Y., et al.: Mutual consistency learning for semi-supervised medical image segmentation. Med. Image Anal. 81, 102530 (2022)","journal-title":"Med. Image Anal."},{"key":"30_CR17","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-031-16443-9_4","volume-title":"MICCAI 2022, Part III","author":"Y Wu","year":"2022","unstructured":"Wu, Y., Wu, Z., Wu, Q., Ge, Z., Cai, J.: Exploring smoothness and class-separation for semi-supervised medical image segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part III. LNCS, vol. 13435, pp. 34\u201343. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16443-9_4"},{"key":"30_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101832","volume":"67","author":"Z Xiong","year":"2021","unstructured":"Xiong, Z., et al.: A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832 (2021)","journal-title":"Med. Image Anal."},{"key":"30_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"605","DOI":"10.1007\/978-3-030-32245-8_67","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","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. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 605\u2013613. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_67"},{"key":"30_CR20","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"},{"key":"30_CR21","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/978-3-031-72652-1_14","volume-title":"ECCV 2024","author":"Z Zhao","year":"2024","unstructured":"Zhao, Z., Wang, Z., Wang, L., Yu, D., Yuan, Y., Zhou, L.: Alternate diverse teaching for semi-supervised medical image segmentation. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) ECCV 2024. LNCS, vol. 15063, pp. 227\u2013243. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-72652-1_14"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-04937-7_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T05:40:45Z","timestamp":1758260445000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04937-7_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"ISBN":["9783032049360","9783032049377"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04937-7_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"20 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}