{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T18:59:04Z","timestamp":1757617144312,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784950"},{"type":"electronic","value":"9789819784967"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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":[[2025]]},"DOI":"10.1007\/978-981-97-8496-7_38","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T02:04:04Z","timestamp":1730513044000},"page":"545-559","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Competing Dual-Network with Pseudo-Supervision Rectification for Semi-Supervised Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Ping","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Bingwen","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Meiyu","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"38_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, pp. 11514\u201311524 (2023)","DOI":"10.1109\/CVPR52729.2023.01108"},{"issue":"2","key":"38_CR2","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1109\/TMI.2022.3184675","volume":"42","author":"C Chen","year":"2022","unstructured":"Chen, C., Zhou, K., Wang, Z., Xiao, R.: Generative consistency for semi-supervised cerebrovascular segmentation from tof-mra. IEEE Trans. Med. Imaging 42(2), 346\u2013353 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"38_CR3","doi-asserted-by":"crossref","unstructured":"Gao, S., Zhang, Z., Ma, J., Li, Z., Zhang, S.: Correlation-aware mutual learning for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 98\u2013108. Springer (2023)","DOI":"10.1007\/978-3-031-43907-0_10"},{"issue":"8","key":"38_CR4","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., Teng, Q., 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":"38_CR5","doi-asserted-by":"crossref","unstructured":"Han, K., Sheng, V.S., Song, Y., Liu, Y., Qiu, C., Ma, S., Liu, Z.: Deep semi-supervised learning for medical image segmentation: a review. Expert Syst. Appl. 123052 (2024)","DOI":"10.1016\/j.eswa.2023.123052"},{"key":"38_CR6","doi-asserted-by":"crossref","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. 107840 (2023)","DOI":"10.1016\/j.compbiomed.2023.107840"},{"key":"38_CR7","unstructured":"Kim, J.H., Choo, W., Song, H.O.: Puzzle mix: exploiting saliency and local statistics for optimal mixup. In: International Conference on Machine Learning, pp. 5275\u20135285. PMLR (2020)"},{"key":"38_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104239","volume":"80","author":"X Li","year":"2023","unstructured":"Li, X., Peng, Y., Xu, M.: Patch-shuffle-based semi-supervised segmentation of bone computed tomography via consistent learning. Biomed. Signal Process. Control 80, 104239 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"Lu, W., Lei, J., Qiu, P., Sheng, R., Zhou, J., Lu, X., Yang, Y.: Upcol: uncertainty-informed prototype consistency learning for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 662\u2013672. Springer (2023)","DOI":"10.1007\/978-3-031-43901-8_63"},{"key":"38_CR10","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"},{"issue":"3","key":"38_CR11","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., Qi, L., 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":"38_CR12","doi-asserted-by":"crossref","unstructured":"Tang, F., Xu, Z., Huang, Q., Wang, J., Hou, X., Su, J., Liu, J.: Duat: dual-aggregation transformer network for medical image segmentation. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 343\u2013356. Springer (2023)","DOI":"10.1007\/978-981-99-8469-5_27"},{"key":"38_CR13","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Tu, Y., Li, X., Zhong, Y., Mei, H.: Semi-supervised medical image segmentation based on multi-scale knowledge discovery and multi-task ensemble. In: Chinese Conference on Pattern Recognition and Computer Vision (PRCV), pp. 209\u2013222. Springer (2023)","DOI":"10.1007\/978-981-99-8558-6_18"},{"key":"38_CR15","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhan, B., Zu, C., Wu, X., Zhou, J., Zhou, L., Wang, Y.: Tripled-uncertainty guided mean teacher model for semi-supervised medical image segmentation. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, Sept 27\u2013Oct 1, 2021, Proceedings, Part II 24, pp. 450\u2013460. Springer (2021)","DOI":"10.1007\/978-3-030-87196-3_42"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, J., Zhu, L., Fu, H., Li, P., Cheng, G., Feng, Z., Li, S., Heng, P.A.: Dual multiscale mean teacher network for semi-supervised infection segmentation in chest ct volume for covid-19. IEEE Trans. Cybern. (2022)","DOI":"10.1109\/TCYB.2022.3223528"},{"key":"38_CR17","doi-asserted-by":"crossref","unstructured":"Wang, R., Wu, Y., Chen, H., Wang, L., Meng, D.: Neighbor matching for semi-supervised learning. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, Sept 27\u2013Oct 1, 2021, Proceedings, Part II 24, pp. 439\u2013449. Springer (2021)","DOI":"10.1007\/978-3-030-87196-3_41"},{"key":"38_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Y., Xiao, B., Bi, X., Li, W., Gao, X.: Mcf: mutual correction framework for semi-supervised medical image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15651\u201315660 (2023)","DOI":"10.1109\/CVPR52729.2023.01502"},{"key":"38_CR19","doi-asserted-by":"publisher","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":"38_CR20","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, pp. 34\u201343. Springer (2022)","DOI":"10.1007\/978-3-031-16443-9_4"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Xiang, J., Qiu, P., Yang, Y.: Fussnet: fusing two sources of uncertainty for semi-supervised medical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 481\u2013491. Springer (2022)","DOI":"10.1007\/978-3-031-16452-1_46"},{"key":"38_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101832","volume":"67","author":"Z Xiong","year":"2021","unstructured":"Xiong, Z., Xia, Q., Hu, Z., Huang, N., Bian, C., Zheng, Y., Vesal, S., Ravikumar, N., Maier, A., Yang, X., 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":"38_CR23","doi-asserted-by":"crossref","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)","DOI":"10.1016\/j.media.2023.102880"},{"key":"38_CR24","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: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, Oct 13\u201317, 2019, Proceedings, Part II 22, pp. 605\u2013613. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"38_CR25","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":"38_CR26","doi-asserted-by":"crossref","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. (2023)","DOI":"10.1109\/TPAMI.2023.3240886"},{"key":"38_CR27","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"38_CR28","doi-asserted-by":"crossref","unstructured":"Zhao, X., Qi, Z., Wang, S., Wang, Q., Wu, X., Mao, Y., Zhang, L.: Rcps: rectified contrastive pseudo supervision for semi-supervised medical image segmentation. IEEE J. Biomed. Health Inform. (2023)","DOI":"10.1109\/JBHI.2023.3322590"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8496-7_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T23:41:38Z","timestamp":1757115698000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8496-7_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819784950","9789819784967"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8496-7_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}