{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T22:10:42Z","timestamp":1785276642442,"version":"3.55.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031721137","type":"print"},{"value":"9783031721144","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-72114-4_65","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:01:43Z","timestamp":1727874103000},"page":"681-691","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Unsupervised Domain Adaptation Using Soft-Labeled Contrastive Learning with\u00a0Reversed Monte Carlo Method for\u00a0Cardiac Image Segmentation"],"prefix":"10.1007","author":[{"given":"Mingxuan","family":"Gu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mareike","family":"Thies","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyuan","family":"Mei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabian","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingcheng","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yipeng","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoya","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sulaiman","family":"Vesal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ronak","family":"Kosti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dennis","family":"Possart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonas","family":"Utz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Maier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"65_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1007\/978-3-030-32692-0_15","volume-title":"Machine Learning in Medical Imaging","author":"J Cai","year":"2019","unstructured":"Cai, J., Xia, Y., Yang, D., Xu, D., Yang, L., Roth, H.: End-to-end adversarial shape learning for abdomen organ deep segmentation. In: Suk, H.-I., Liu, M., Yan, P., Lian, C. (eds.) MLMI 2019. LNCS, vol. 11861, pp. 124\u2013132. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32692-0_15"},{"key":"65_CR2","unstructured":"Chaitanya, K., Erdil, E., Karani, N., Konukoglu, E.: Contrastive learning of global and local features for medical image segmentation with limited annotations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 12546\u201312558 (2020)"},{"key":"65_CR3","unstructured":"Haq, M.M., Huang, J.: Adversarial domain adaptation for cell segmentation. In: Medical Imaging with Deep Learning, pp. 277\u2013287. PMLR (2020)"},{"key":"65_CR4","doi-asserted-by":"crossref","unstructured":"Harrison, R.L.: Introduction to Monte Carlo simulation. In: AIP Conference Proceedings, vol.\u00a01204, pp. 17\u201321. American Institute of Physics (2010)","DOI":"10.1063\/1.3295638"},{"key":"65_CR5","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"65_CR6","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.neunet.2023.05.014","volume":"165","author":"S Kuang","year":"2023","unstructured":"Kuang, S., et al.: MSCDA: multi-level semantic-guided contrast improves unsupervised domain adaptation for breast MRI segmentation in small datasets. Neural Netw. 165, 119\u2013134 (2023)","journal-title":"Neural Netw."},{"key":"65_CR7","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1007\/978-3-031-20056-4_3","volume-title":"ECCV 2022, Part XXX","author":"G Lee","year":"2022","unstructured":"Lee, G., Eom, C., Lee, W., Park, H., Ham, B.: Bi-directional contrastive learning for domain adaptive semantic segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022, Part XXX. LNCS, vol. 13690, pp. 38\u201355. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20056-4_3"},{"key":"65_CR8","first-page":"1","volume":"61","author":"C Liang","year":"2023","unstructured":"Liang, C., Cheng, B., Xiao, B., Dong, Y., Chen, J.: Multilevel heterogeneous domain adaptation method for remote sensing image segmentation. IEEE Trans. Geosci. Remote Sens. 61, 1\u201316 (2023)","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"issue":"2","key":"65_CR9","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1109\/JBHI.2022.3140853","volume":"26","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Zhu, Z., Zheng, S., Liu, Y., Zhou, J., Zhao, Y.: Margin preserving self-paced contrastive learning towards domain adaptation for medical image segmentation. IEEE J. Biomed. Health Inform. 26(2), 638\u2013647 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"65_CR10","doi-asserted-by":"crossref","unstructured":"Marsden, R.A., Bartler, A., D\u00f6bler, M., Yang, B.: Contrastive learning and self-training for unsupervised domain adaptation in semantic segmentation. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp.\u00a01\u20138. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892322"},{"key":"65_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-030-68799-1_8","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"D Mugnai","year":"2021","unstructured":"Mugnai, D., Pernici, F., Turchini, F., Del Bimbo, A.: Soft pseudo-labeling semi-supervised learning applied to fine-grained visual classification. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12664, pp. 102\u2013110. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68799-1_8"},{"key":"65_CR12","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.E.: When does label smoothing help? Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"65_CR13","doi-asserted-by":"crossref","unstructured":"Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5552\u20135560 (2018)","DOI":"10.1109\/CVPR.2018.00582"},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"Thulasidasan, S., Chennupati, G., Bilmes, J.A., Bhattacharya, T., Michalak, S.: On mixup training: improved calibration and predictive uncertainty for deep neural networks. In: Advances in Neural Information Processing Systems, vol. 32 (2019)","DOI":"10.2172\/1525811"},{"key":"65_CR15","doi-asserted-by":"crossref","unstructured":"Tsai, Y., Hung, W., Schulter, S., Sohn, K., Yang, M., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: CVPR, pp. 7472\u20137481 (2018)","DOI":"10.1109\/CVPR.2018.00780"},{"issue":"7","key":"65_CR16","doi-asserted-by":"publisher","first-page":"1838","DOI":"10.1109\/TMI.2021.3066683","volume":"40","author":"S Vesal","year":"2021","unstructured":"Vesal, S., Gu, M., Kosti, R., Maier, A., Ravikumar, N.: Adapt everywhere: unsupervised adaptation of point-clouds and entropy minimization for multi-modal cardiac image segmentation. IEEE Trans. Med. Imaging 40(7), 1838\u20131851 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"65_CR17","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\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2517\u20132526 (2019)","DOI":"10.1109\/CVPR.2019.00262"},{"key":"65_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1007\/978-3-030-32239-7_12","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"S Wang","year":"2019","unstructured":"Wang, S., Yu, L., Li, K., Yang, X., Fu, C.-W., Heng, P.-A.: Boundary and entropy-driven adversarial learning for fundus image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11764, pp. 102\u2013110. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32239-7_12"},{"key":"65_CR19","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: Semi-supervised semantic segmentation using unreliable pseudo-labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4248\u20134257 (2022)","DOI":"10.1109\/CVPR52688.2022.00421"},{"issue":"12","key":"65_CR20","doi-asserted-by":"publisher","first-page":"4274","DOI":"10.1109\/TMI.2020.3016144","volume":"39","author":"F Wu","year":"2020","unstructured":"Wu, F., Zhuang, X.: CF distance: a new domain discrepancy metric and application to explicit domain adaptation for cross-modality cardiac image segmentation. IEEE Trans. Med. Imaging 39(12), 4274\u20134285 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"65_CR21","first-page":"9004","volume":"45","author":"B Xie","year":"2023","unstructured":"Xie, B., Li, S., Li, M., Liu, C.H., Huang, G., Wang, G.: Sepico: semantic-guided pixel contrast for domain adaptive semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 45(7), 9004\u20139021 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"65_CR22","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"65_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, X., et al.: Contrastive learning for label efficient semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10623\u201310633 (2021)","DOI":"10.1109\/ICCV48922.2021.01045"},{"issue":"12","key":"65_CR24","doi-asserted-by":"publisher","first-page":"2933","DOI":"10.1109\/TPAMI.2018.2869576","volume":"41","author":"X Zhuang","year":"2018","unstructured":"Zhuang, X.: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Trans. Pattern Anal. Mach. Intell. 41(12), 2933\u20132946 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"65_CR25","doi-asserted-by":"crossref","unstructured":"Zhuang, X., et al.: Evaluation of algorithms for multi-modality whole heart segmentation: an open-access grand challenge. Med. Image Anal. 58, 101537 (2019)","DOI":"10.1016\/j.media.2019.101537"},{"key":"65_CR26","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.media.2016.02.006","volume":"31","author":"X Zhuang","year":"2016","unstructured":"Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77\u201387 (2016)","journal-title":"Med. Image Anal."},{"key":"65_CR27","doi-asserted-by":"crossref","unstructured":"Zou, Y., Yu, Z., Liu, X., Kumar, B., Wang, J.: Confidence regularized self-training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5982\u20135991 (2019)","DOI":"10.1109\/ICCV.2019.00608"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72114-4_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T13:08:50Z","timestamp":1727874530000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72114-4_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031721137","9783031721144"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72114-4_65","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that\u00a0are 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":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","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":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}