{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T09:09:07Z","timestamp":1790413747183,"version":"4.1.0"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1016\/j.media.2025.103575","type":"journal-article","created":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T02:43:23Z","timestamp":1744425803000},"page":"103575","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":11,"special_numbering":"C","title":["Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation"],"prefix":"10.1016","volume":"103","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6172-5190","authenticated-orcid":false,"given":"Alvaro","family":"Gomariz","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yusuke","family":"Kikuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4902-672X","authenticated-orcid":false,"given":"Yun Yvonna","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Albrecht","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Maunz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniela","family":"Ferrara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanxiang","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-7373","authenticated-orcid":false,"given":"Orcun","family":"Goksel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.media.2025.103575_b1","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1109\/TMI.2019.2901398","article-title":"RETOUCH: The retinal OCT fluid detection and segmentation benchmark and challenge","volume":"38","author":"Bogunovic","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103575_b2","article-title":"Signature verification using a \u201csiamese\u201d time delay neural network","volume":"6","author":"Bromley","year":"1993","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2025.103575_b3","doi-asserted-by":"crossref","unstructured":"Caron, M., Touvron, H., Misra, I., J\u00e9gou, H., Mairal, J., Bojanowski, P., Joulin, A., 2021. Emerging properties in self-supervised vision transformers. In: IEEE Int Conf on Computer Vision. ICCV, pp. 9650\u20139660.","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"10.1016\/j.media.2025.103575_b4","first-page":"12546","article-title":"Contrastive learning of global and local features for medical image segmentation with limited annotations","volume":"vol. 33","author":"Chaitanya","year":"2020"},{"key":"10.1016\/j.media.2025.103575_b5","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K., 2021. Exploring Simple Siamese Representation Learning. In: IEEE Conf on Computer Vision and Pattern Recognition. CVPR, pp. 15750\u201315758.","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"10.1016\/j.media.2025.103575_b6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G., 2020a. A Simple Framework for Contrastive Learning of Visual Representations. In: Int Conf on Machine Learning. ICML, pp. 1597\u20131607."},{"key":"10.1016\/j.media.2025.103575_b7","series-title":"Advances in Neural Information Processing Systems (NeurIPS)","first-page":"22243","article-title":"Big self-supervised models are strong semi-supervised learners","author":"Chen","year":"2020"},{"key":"10.1016\/j.media.2025.103575_b8","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, C., Liu, L., Feng, C., Dong, C., Luo, Y., Wan, X., 2021. USCL: Pretraining Deep Ultrasound Image Diagnosis Model Through Video Contrastive Representation Learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI, pp. 627\u2013637.","DOI":"10.1007\/978-3-030-87237-3_60"},{"key":"10.1016\/j.media.2025.103575_b9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L., 2009. ImageNet: A large-scale hierarchical image database. In: IEEE Conf on Computer Vision and Pattern Recognition. CVPR, pp. 248\u2013255.","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"9","key":"10.1016\/j.media.2025.103575_b10","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"Fauw","year":"2018","journal-title":"Nature Med."},{"issue":"9","key":"10.1016\/j.media.2025.103575_b11","article-title":"The development, commercialization, and impact of optical coherence tomography","volume":"57","author":"Fujimoto","year":"2016","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"issue":"7","key":"10.1016\/j.media.2025.103575_b12","first-page":"2053","article-title":"A unified deep learning approach for OCT segmentation from different devices and retinal diseases","volume":"63","author":"Gomariz","year":"2022","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"10.1016\/j.media.2025.103575_b13","doi-asserted-by":"crossref","unstructured":"Gomariz, A., Lu, H., Li, Y.Y., Albrecht, T., Maunz, A., Benmansour, F., Valcarcel, A.M., Luu, J., Ferrara, D., Goksel, O., 2022b. Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI, pp. 351\u2013361.","DOI":"10.1007\/978-3-031-16452-1_34"},{"key":"10.1016\/j.media.2025.103575_b14","first-page":"21271","article-title":"Bootstrap your own latent-a new approach to self-supervised learning","volume":"vol. 33","author":"Grill","year":"2020"},{"key":"10.1016\/j.media.2025.103575_b15","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R., 2020. Momentum Contrast for Unsupervised Visual Representation Learning. In: IEEE Conf on Computer Vision and Pattern Recognition. CVPR, pp. 9729\u20139738.","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"10.1016\/j.media.2025.103575_b16","first-page":"18661","article-title":"Supervised contrastive learning","volume":"vol. 33","author":"Khosla","year":"2020"},{"key":"10.1016\/j.media.2025.103575_b17","unstructured":"Kingma, D.P., Ba, J., 2015. Adam: A Method for Stochastic Optimization. In: Int Conf on Learning Representations. ICLR."},{"key":"10.1016\/j.media.2025.103575_b18","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"699","article-title":"Noise transfer for unsupervised domain adaptation of retinal oct images","author":"Koch","year":"2022"},{"key":"10.1016\/j.media.2025.103575_b19","series-title":"SVDNA public implementation","author":"Koch","year":"2022"},{"key":"10.1016\/j.media.2025.103575_b20","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.media.2016.07.009","article-title":"ISLES 2015-A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI","volume":"35","author":"Maier","year":"2017","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/j.media.2025.103575_b21","doi-asserted-by":"crossref","first-page":"524","DOI":"10.3390\/jpm11060524","article-title":"Accuracy of a machine-learning algorithm for detecting and classifying choroidal neovascularization on spectral-domain optical coherence tomography","volume":"11","author":"Maunz","year":"2021","journal-title":"J. Pers. Med."},{"key":"10.1016\/j.media.2025.103575_b22","series-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"10.1016\/j.media.2025.103575_b23","doi-asserted-by":"crossref","unstructured":"Peng, K.-C., Wu, Z., Ernst, J., 2018. Zero-shot deep domain adaptation. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 764\u2013781.","DOI":"10.1007\/978-3-030-01252-6_47"},{"issue":"6","key":"10.1016\/j.media.2025.103575_b24","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1109\/TMI.2021.3059726","article-title":"Segmentation-renormalized deep feature modulation for unpaired image harmonization","volume":"40","author":"Ren","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103575_b25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T., 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI, pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"11","key":"10.1016\/j.media.2025.103575_b26","first-page":"PB094","article-title":"A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT","volume":"60","author":"Sahni","year":"2019","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"issue":"4","key":"10.1016\/j.media.2025.103575_b27","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.ophtha.2017.10.031","article-title":"Fully automated detection and quantification of macular fluid in OCT using deep learning","volume":"125","author":"Schlegl","year":"2018","journal-title":"Ophthalmology"},{"key":"10.1016\/j.media.2025.103575_b28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.preteyeres.2015.07.007","article-title":"A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration","volume":"50","author":"Schmidt-Erfurth","year":"2016","journal-title":"Prog. Retin. Eye Res."},{"key":"10.1016\/j.media.2025.103575_b29","doi-asserted-by":"crossref","unstructured":"Seeb\u00f6ck, P., Romo-Bucheli, D., Waldstein, S., Bogunovi\u0107, H., Orlando, J.I., Gerendas, B.S., Langs, G., Schmidt-Erfurth, U., 2019. Using CycleGANs for effectively reducing image variability across oct devices and improving retinal fluid segmentation. In: IEEE Int Symp on Biomedical Imaging. ISBI, pp. 605\u2013609.","DOI":"10.1109\/ISBI.2019.8759158"},{"key":"10.1016\/j.media.2025.103575_b30","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.05.083","article-title":"Deep visual domain adaptation: A survey","volume":"312","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.media.2025.103575_b31","doi-asserted-by":"crossref","unstructured":"Wu, Y., He, K., 2018. Group normalization. In: European Conference on Computer Vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01261-8_1"},{"key":"10.1016\/j.media.2025.103575_b32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., Liu, H., 2022. Towards unsupervised domain generalization. In: IEEE Conf on Computer Vision and Pattern Recognition. CVPR, pp. 4910\u20134920.","DOI":"10.1109\/CVPR52688.2022.00486"},{"issue":"4","key":"10.1016\/j.media.2025.103575_b33","first-page":"4396","article-title":"Domain generalization: A survey","volume":"45","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525001227?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525001227?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T13:12:57Z","timestamp":1774530777000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841525001227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7]]},"references-count":33,"alternative-id":["S1361841525001227"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2025.103575","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2025,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2025.103575","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103575"}}