{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:54:15Z","timestamp":1742918055695,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031720826"},{"type":"electronic","value":"9783031720833"}],"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\/978-3-031-72083-3_72","type":"book-chapter","created":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:01:42Z","timestamp":1728842502000},"page":"775-785","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertainty-Aware Meta-weighted Optimization Framework for\u00a0Domain-Generalized Medical Image Segmentation"],"prefix":"10.1007","author":[{"given":"Seok-Hwan","family":"Oh","sequence":"first","affiliation":[]},{"given":"Guil","family":"Jung","sequence":"additional","affiliation":[]},{"given":"Sang-Yun","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Myeong-Gee","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Young-Min","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Hyeon-Jik","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Hyuk-Sool","family":"Kwon","sequence":"additional","affiliation":[]},{"given":"Hyeon-Min","family":"Bae","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,14]]},"reference":[{"key":"72_CR1","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","volume":"7","author":"C Chen","year":"2020","unstructured":"Chen, C., Qin, C., Qiu, H., Tarroni, G., Duan, J., Bai, W., Rueckert, D.: Deep learning for cardiac image segmentation: a review. Frontiers in Cardiovascular Medicine 7, \u00a025 (2020)","journal-title":"Frontiers in Cardiovascular Medicine"},{"key":"72_CR2","doi-asserted-by":"publisher","first-page":"34442","DOI":"10.1109\/ACCESS.2021.3059595","volume":"9","author":"A Degerli","year":"2021","unstructured":"Degerli, A., Zabihi, M., Kiranyaz, S., Hamid, T., Mazhar, R., Hamila, R., Gabbouj, M.: Early detection of myocardial infarction in low-quality echocardiography. IEEE Access 9, 34442\u201334453 (2021)","journal-title":"IEEE Access"},{"key":"72_CR3","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"72_CR4","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33, 6840\u20136851 (2020)","journal-title":"Advances in neural information processing systems"},{"issue":"3","key":"72_CR5","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1093\/ehjci\/jev014","volume":"16","author":"RM Lang","year":"2015","unstructured":"Lang, R.M., Badano, L.P., Mor-Avi, V., Afilalo, J., Armstrong, A., Ernande, L., Flachskampf, F.A., Foster, E., Goldstein, S.A., Kuznetsova, T., et\u00a0al.: Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the american society of echocardiography and the european association of cardiovascular imaging. European Heart Journal-Cardiovascular Imaging 16(3), 233\u2013271 (2015)","journal-title":"European Heart Journal-Cardiovascular Imaging"},{"issue":"9","key":"72_CR6","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc, S., Smistad, E., Pedrosa, J., \u00d8stvik, A., Cervenansky, F., Espinosa, F., Espeland, T., Berg, E.A.R., Jodoin, P.M., Grenier, T., et\u00a0al.: Deep learning for segmentation using an open large-scale dataset in 2d echocardiography. IEEE transactions on medical imaging 38(9), 2198\u20132210 (2019)","journal-title":"IEEE transactions on medical imaging"},{"issue":"12","key":"72_CR7","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1109\/TIP.2010.2069690","volume":"19","author":"C Li","year":"2010","unstructured":"Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE transactions on image processing 19(12), 3243\u20133254 (2010)","journal-title":"IEEE transactions on image processing"},{"key":"72_CR8","doi-asserted-by":"crossref","unstructured":"Li, D., Yang, Y., Song, Y.Z., Hospedales, T.: Learning to generalize: Meta-learning for domain generalization. In: Proceedings of the AAAI conference on artificial intelligence. vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.11596"},{"key":"72_CR9","unstructured":"Li, X., Dai, Y., Ge, Y., Liu, J., Shan, Y., Duan, L.Y.: Uncertainty modeling for out-of-distribution generalization. arXiv preprint arXiv:2202.03958 (2022)"},{"issue":"7802","key":"72_CR10","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","volume":"580","author":"D Ouyang","year":"2020","unstructured":"Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C.P., Heidenreich, P.A., Harrington, R.A., Liang, D.H., Ashley, E.A., et\u00a0al.: Video-based ai for beat-to-beat assessment of cardiac function. Nature 580(7802), 252\u2013256 (2020)","journal-title":"Nature"},{"issue":"7","key":"72_CR11","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.1109\/83.847836","volume":"9","author":"MR Rezaee","year":"2000","unstructured":"Rezaee, M.R., Van\u00a0der Zwet, P.M., Lelieveldt, B., Van\u00a0der Geest, R.J., Reiber, J.H.: A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering. IEEE transactions on image processing 9(7), 1238\u20131248 (2000)","journal-title":"IEEE transactions on image processing"},{"key":"72_CR12","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"72_CR13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. pp. 234\u2013241. Springer (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"6","key":"72_CR14","doi-asserted-by":"publisher","first-page":"1716","DOI":"10.1109\/58.971725","volume":"48","author":"PM Shankar","year":"2001","unstructured":"Shankar, P.M.: Ultrasonic tissue characterization using a generalized nakagami model. IEEE transactions on ultrasonics, ferroelectrics, and frequency control 48(6), 1716\u20131720 (2001)","journal-title":"IEEE transactions on ultrasonics, ferroelectrics, and frequency control"},{"key":"72_CR15","doi-asserted-by":"crossref","unstructured":"Song, H., Kim, M., Park, D., Shin, Y., Lee, J.G.: Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems (2022)","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"72_CR16","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"72_CR17","doi-asserted-by":"crossref","unstructured":"Stojanovski, D., Hermida, U., Lamata, P., Beqiri, A., Gomez, A.: Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation. arXiv preprint arXiv:2305.05424 (2023)","DOI":"10.1007\/978-3-031-44521-7_4"},{"key":"72_CR18","doi-asserted-by":"publisher","first-page":"17594","DOI":"10.1109\/ACCESS.2023.3246762","volume":"11","author":"C Tiago","year":"2023","unstructured":"Tiago, C., Snare, S.R., \u0160prem, J., McLeod, K.: A domain translation framework with an adversarial denoising diffusion model to generate synthetic datasets of echocardiography images. IEEE Access 11, 17594\u201317602 (2023)","journal-title":"IEEE Access"},{"key":"72_CR19","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","volume":"312","author":"M Wang","year":"2018","unstructured":"Wang, M., Deng, W.: Deep visual domain adaptation: A survey. Neurocomputing 312, 135\u2013153 (2018)","journal-title":"Neurocomputing"},{"issue":"3","key":"72_CR20","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1109\/83.661186","volume":"7","author":"C Xu","year":"1998","unstructured":"Xu, C., Prince, J.L.: Snakes, shapes, and gradient vector flow. IEEE Transactions on image processing 7(3), 359\u2013369 (1998)","journal-title":"IEEE Transactions on image processing"},{"key":"72_CR21","doi-asserted-by":"crossref","unstructured":"Xu, Q., Zhang, R., Zhang, Y., Wang, Y., Tian, Q.: A fourier-based framework for domain generalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 14383\u201314392 (2021)","DOI":"10.1109\/CVPR46437.2021.01415"},{"key":"72_CR22","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)"},{"key":"72_CR23","doi-asserted-by":"crossref","unstructured":"Zhou, K., Liu, Z., Qiao, Y., Xiang, T., Loy, C.C.: Domain generalization: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2022)","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"72_CR24","unstructured":"Zhou, K., Yang, Y., Qiao, Y., Xiang, T.: Domain generalization with mixstyle. arXiv preprint arXiv:2104.02008 (2021)"},{"key":"72_CR25","unstructured":"Zhou, S.K., Rueckert, D., Fichtinger, G.: Handbook of medical image computing and computer assisted intervention. Academic Press (2019)"},{"key":"72_CR26","doi-asserted-by":"crossref","unstructured":"Zuluaga, M.A., Biffi, B., Taylor, A.M., Schievano, S., Vercauteren, T., Ourselin, S.: Strengths and pitfalls of whole-heart atlas-based segmentation in congenital heart disease patients. In: Reconstruction, Segmentation, and Analysis of Medical Images: First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers 1. pp. 139\u2013146. Springer (2017)","DOI":"10.1007\/978-3-319-52280-7_14"}],"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\/978-3-031-72083-3_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T18:15:33Z","timestamp":1728843333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72083-3_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031720826","9783031720833"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72083-3_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"14 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"S-H. Oh and Y-M. Kim are under contracts with Ministry of Korea National Defense. Y-M. Kim, H-J. Lee and S-Y. Kim are under scholarship from the Korea Government Scholarship Program.","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"}}]}}