{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:18:05Z","timestamp":1772911085873,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819784950","type":"print"},{"value":"9789819784967","type":"electronic"}],"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_11","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T02:02:22Z","timestamp":1730512942000},"page":"148-162","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Class-Aware Cross Pseudo Supervision Framework for\u00a0Semi-Supervised Multi-organ Segmentation in\u00a0Abdominal CT Scans"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-6633-3541","authenticated-orcid":false,"given":"Deqian","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9234-3594","authenticated-orcid":false,"given":"Haochen","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0240-3033","authenticated-orcid":false,"given":"Gaojie","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4061-2100","authenticated-orcid":false,"given":"Hui","family":"Meng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3692-2088","authenticated-orcid":false,"given":"Lijun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N.E., McGuinness, K.: Pseudo-labeling and confirmation bias in deep semi-supervised learning (2020)","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"11_CR2","first-page":"32424","volume":"35","author":"B Chen","year":"2022","unstructured":"Chen, B., Jiang, J., Wang, X., Wan, P., Wang, J., Long, M.: Debiased self-training for semi-supervised learning. Adv. Neural. Inf. Process. Syst. 35, 32424\u201332437 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"11_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":"11_CR4","doi-asserted-by":"crossref","unstructured":"Christ, P.F., Elshaer, M.E.A., Ettlinger, F., Tatavarty, S., Bickel, M., Bilic, P., Rempfler, M., Armbruster, M., Hofmann, F., D\u2019Anastasi, M.: Automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17\u201321, 2016, Proceedings, Part II 19, pp. 415\u2013423. Springer (2016)","DOI":"10.1007\/978-3-319-46723-8_48"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3d u-net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17\u201321, 2016, Proceedings, Part II 19, pp. 424\u2013432. Springer (2016)","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"11_CR6","unstructured":"Ji, Y., Bai, H., Yang, J., Ge, C., Zhu, Y., Zhang, R., Li, Z., Zhang, L., Ma, W., Wan, X., Luo, P.: Amos: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation (2022)"},{"key":"11_CR7","unstructured":"Jin, G., Yi, X., Yang, P., Zhang, L., Schewe, S., Huang, X.: Weight expansion: a new perspective on dropout and generalization. Trans. Mach. Learn. Res. (2022)"},{"key":"11_CR8","unstructured":"Jin, G., Yi, X., Zhang, L., Zhang, L., Schewe, S., Huang, X.: How does weight correlation affect the generalisation ability of deep neural networks. NeurIPS (2020)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Kaur, H., Kaur, N., Neeru, N.: Evolution of multiorgan segmentation techniques from traditional to deep learning in abdominal ct images\u2014a systematic review. Displays 73, 102223 (2022)","DOI":"10.1016\/j.displa.2022.102223"},{"key":"11_CR10","unstructured":"Lee, D.H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on Challenges in Representation Learning, ICML. vol.\u00a03, p.\u00a0896. Atlanta (2013)"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"11_CR12","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":"11_CR13","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":"11_CR14","doi-asserted-by":"publisher","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., Metaxas, D.N., Zhang, S.: Semi-supervised medical image segmentation via uncertainty rectified pyramid consistency. Med. Image Anal. 80, 102517 (2022)","journal-title":"Med. Image Anal."},{"key":"11_CR15","doi-asserted-by":"crossref","unstructured":"Ma, J., Zhang, Y., Gu, S., Ge, C., Ma, S., Young, A., Zhu, C., Meng, K., Yang, X., Huang, Z.: Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge. arXiv preprint arXiv:2308.05862 (2023)","DOI":"10.1016\/S2589-7500(24)00154-7"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Shen, Z., Cao, P., Yang, H., Liu, X., Yang, J., Zaiane, O.R.: Co-training with high-confidence pseudo labels for semi-supervised medical image segmentation (2023)","DOI":"10.24963\/ijcai.2023\/467"},{"key":"11_CR17","doi-asserted-by":"publisher","unstructured":"Sugino, T., Kawase, T., Onogi, S., Kin, T., Saito, N., Nakajima, Y.: Loss weightings for improving imbalanced brain structure segmentation using fully convolutional networks. Healthcare 9(8) (2021). https:\/\/doi.org\/10.3390\/healthcare9080938, https:\/\/www.mdpi.com\/2227-9032\/9\/8\/938","DOI":"10.3390\/healthcare9080938"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Summers, R.M.: Progress in fully automated abdominal ct interpretation. Am. J. Roentgenol. 207(1), 67\u201379 (2016)","DOI":"10.2214\/AJR.15.15996"},{"key":"11_CR19","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":"11_CR20","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, X.: Dhc: Dual-debiased heterogeneous co-training framework for class-imbalanced semi-supervised medical image segmentation (2023)","DOI":"10.1007\/978-3-031-43898-1_56"},{"key":"11_CR21","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). https:\/\/doi.org\/10.1016\/j.media.2022.102530. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1361841522001773","journal-title":"Med. Image Anal."},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Yang, L., Qi, L., Feng, L., Zhang, W., Shi, Y.: Revisiting weak-to-strong consistency in semi-supervised semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7236\u20137246 (2023)","DOI":"10.1109\/CVPR52729.2023.00699"},{"key":"11_CR23","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, October 13\u201317, 2019, Proceedings, Part II 22, pp. 605\u2013613. Springer (2019)","DOI":"10.1007\/978-3-030-32245-8_67"},{"key":"11_CR24","unstructured":"Zhang, B., Wang, Y., Hou, W., Wu, H., Wang, J., Okumura, M., Shinozaki, T.: Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling (2022)"},{"key":"11_CR25","doi-asserted-by":"publisher","first-page":"4259","DOI":"10.1007\/s10462-019-09792-7","volume":"53","author":"M Zhang","year":"2020","unstructured":"Zhang, M., Zhou, Y., Zhao, J., Man, Y., Liu, B., Yao, R.: A survey of semi-and weakly supervised semantic segmentation of images. Artif. Intell. Rev. 53, 4259\u20134288 (2020)","journal-title":"Artif. Intell. Rev."},{"key":"11_CR26","doi-asserted-by":"publisher","unstructured":"Zhou, Y., Wang, Y., Tang, P., Bai, S., Shen, W., Fishman, E., Yuille, A.: Semi-supervised 3d abdominal multi-organ segmentation via deep multi-planar co-training. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 121\u2013140 (2019). https:\/\/doi.org\/10.1109\/WACV.2019.00020","DOI":"10.1109\/WACV.2019.00020"}],"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_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,5]],"date-time":"2025-09-05T23:48:35Z","timestamp":1757116115000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8496-7_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819784950","9789819784967"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8496-7_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"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"}}]}}