{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:18:59Z","timestamp":1783185539343,"version":"3.54.6"},"reference-count":174,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100015401","name":"Key Research and Development Projects of Shaanxi Province","doi-asserted-by":"publisher","award":["2025SF-YBXM-424"],"award-info":[{"award-number":["2025SF-YBXM-424"]}],"id":[{"id":"10.13039\/501100015401","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572401"],"award-info":[{"award-number":["62572401"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62322112"],"award-info":[{"award-number":["62322112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.asoc.2026.115314","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T15:10:57Z","timestamp":1777129857000},"page":"115314","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A survey on unsupervised domain adaptation in medical imaging: Methods, datasets, and future outlook"],"prefix":"10.1016","volume":"199","author":[{"given":"Hancun","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hengjun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanlin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linkuan","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leyi","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiangguo","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7553","key":"10.1016\/j.asoc.2026.115314_bib0005","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.asoc.2026.115314_bib0010","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/978-981-15-3383-9_54","article-title":"Deep learning techniques: an overview","author":"Mathew","year":"2021","journal-title":"Advanced Machine Learning Technologies and Applications: Proceedings of AMLTA 2020"},{"key":"10.1016\/j.asoc.2026.115314_bib0015","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0020","doi-asserted-by":"crossref","first-page":"7834","DOI":"10.1109\/TIP.2020.3006377","article-title":"Collaborative unsupervised domain adaptation for medical image diagnosis","volume":"29","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.asoc.2026.115314_bib0025","author":"Sharma"},{"key":"10.1016\/j.asoc.2026.115314_bib0030","first-page":"877","article-title":"A brief review of domain adaptation","author":"Farahani","year":"2021","journal-title":"Advances in Data Science and Information Engineering: Proceedings from ICDATA 2020 and IKE 2020"},{"issue":"3","key":"10.1016\/j.asoc.2026.115314_bib0035","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1109\/TBME.2021.3117407","article-title":"Domain adaptation for medical image analysis: a survey","volume":"69","author":"Guan","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0040","doi-asserted-by":"crossref","DOI":"10.1561\/116.00000192","article-title":"Deep unsupervised domain adaptation: a review of recent advances and perspectives","volume":"11","author":"Liu","year":"2022","journal-title":"APSIPA Trans. Signal Inf. Process."},{"key":"10.1016\/j.asoc.2026.115314_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107912","article-title":"Deep learning for unsupervised domain adaptation in medical imaging: recent advancements and future perspectives","volume":"170","author":"Kumari","year":"2024","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0050","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"Gretton","year":"2012","journal-title":"The journal of machine learning research"},{"key":"10.1016\/j.asoc.2026.115314_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119711","article-title":"A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification","volume":"221","author":"He","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.asoc.2026.115314_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111573","article-title":"Consecutive knowledge meta-adaptation learning for unsupervised medical diagnosis","volume":"291","author":"Zhang","year":"2024","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.asoc.2026.115314_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102707","article-title":"Unsupervised cross-domain functional MRI adaptation for automated major depressive disorder identification","volume":"84","author":"Fang","year":"2023","journal-title":"Med. Image Anal."},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0070","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.1109\/TMI.2023.3318006","article-title":"A Structure-Aware framework of unsupervised Cross-Modality domain adaptation via frequency and spatial knowledge distillation","volume":"42","author":"Liu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0075","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"495","article-title":"Domain-Adaptive 3d medical image synthesis: an efficient unsupervised approach","author":"Hu","year":"2022"},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0080","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/TRPMS.2023.3332619","article-title":"A 3-d Anatomy-Guided Self-Training segmentation framework for unpaired Cross-Modality medical image segmentation","volume":"8","author":"Zhuang","year":"2023","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0085","doi-asserted-by":"crossref","first-page":"3979","DOI":"10.1109\/TMI.2020.3009029","article-title":"Target-Independent domain adaptation for WBC classification using generative latent search","volume":"39","author":"Pandey","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"5","key":"10.1016\/j.asoc.2026.115314_bib0090","doi-asserted-by":"crossref","first-page":"3219","DOI":"10.1109\/TPAMI.2022.3183115","article-title":"Ordinal unsupervised domain adaptation with recursively conditional Gaussian imposed variational disentanglement","volume":"47","author":"Liu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.asoc.2026.115314_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102436","article-title":"Effective Pseudo-Labeling based on heatmap for unsupervised domain adaptation in cell detection","volume":"79","author":"Cho","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0100","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6532","article-title":"Leveraging anatomical consistency for multi-object detection in ultrasound images via source-free unsupervised domain adaptation","volume":"vol. 39","author":"Pu","year":"2025"},{"key":"10.1016\/j.asoc.2026.115314_bib0105","series-title":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","first-page":"4869","article-title":"Cmcd-net: unsupervised domain adaptation with contrastive learning for cross-modality and cross-disease brain lesion segmentation","author":"Chen","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0110","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"58","article-title":"Domain adaptation for unsupervised cancer detection: an application for skin whole slides images from an interhospital dataset","author":"Garc\u00eda-de-la Puente","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0115","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102135","article-title":"Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure","volume":"72","author":"Liu","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0120","doi-asserted-by":"crossref","first-page":"99065","DOI":"10.1109\/ACCESS.2019.2929258","article-title":"PnP-AdaNet: Plug-and-Play adversarial domain adaptation network at unpaired Cross-Modality cardiac segmentation","volume":"7","author":"Dou","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.asoc.2026.115314_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103179","article-title":"Unsupervised model adaptation for source-free segmentation of medical images","volume":"95","author":"Stan","year":"2024","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0130","doi-asserted-by":"crossref","first-page":"2690","DOI":"10.1038\/s41598-024-53311-w","article-title":"Domain adaptation via wasserstein distance and discrepancy metric for chest x-ray image classification","volume":"14","author":"He","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115314_bib0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107207","article-title":"Unsupervised domain adaptation for Covid-19 classification based on balanced slice wasserstein distance","author":"Gu","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115314_bib0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106576","article-title":"Deep reconstruction-recoding network for unsupervised domain adaptation and multi-center generalization in colonoscopy polyp detection","volume":"214","author":"Xu","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.asoc.2026.115314_bib0145","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part I 24","first-page":"293","article-title":"MT-UDA: towards unsupervised Cross-Modality medical image segmentation with limited source labels","author":"Zhao","year":"2021"},{"key":"10.1016\/j.asoc.2026.115314_bib0150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neuroimage.2019.03.026","article-title":"Unsupervised domain adaptation for medical imaging segmentation with self-ensembling","volume":"194","author":"Perone","year":"2019","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.asoc.2026.115314_bib0155","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1109\/TMI.2022.3214766","article-title":"Le-uda: label-efficient unsupervised domain adaptation for medical image segmentation","volume":"42","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102851","article-title":"Attentive continuous generative self-training for unsupervised domain adaptive medical image translation","volume":"88","author":"Liu","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102467","article-title":"Unsupervised domain selective graph convolutional network for preoperative prediction of lymph node metastasis in gastric cancer","volume":"79","author":"Zhang","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0170","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","article-title":"f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks","volume":"54","author":"Schlegl","year":"2019","journal-title":"Med. Image Anal."},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0175","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.1109\/TMI.2018.2842767","article-title":"Unsupervised reverse domain adaptation for synthetic medical images via adversarial training","volume":"37","author":"Mahmood","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126469","article-title":"Unsupervised domain adaptation via style adaptation and boundary enhancement for medical semantic segmentation","volume":"550","author":"Ge","year":"2023","journal-title":"Neurocomputing"},{"issue":"9","key":"10.1016\/j.asoc.2026.115314_bib0185","doi-asserted-by":"crossref","first-page":"5497","DOI":"10.1109\/JBHI.2024.3406447","article-title":"TriLA: Triple-Level alignment based unsupervised domain adaptation for joint segmentation of optic disc and optic CUP","volume":"28","author":"Chen","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"10.1016\/j.asoc.2026.115314_bib0190","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1109\/TRPMS.2024.3391285","article-title":"Structure-Enhanced unsupervised domain adaptation for CT Whole-Brain segmentation","volume":"8","author":"Chen","year":"2024","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"10.1016\/j.asoc.2026.115314_bib0195","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109472","article-title":"Uda-gs: across-center multimodal unsupervised domain adaptation framework for glioma segmentation","volume":"185","author":"Hu","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115314_bib0200","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103275","article-title":"Dual domain distribution disruption with semantics preservation: unsupervised domain adaptation for medical image segmentation","volume":"97","author":"Zheng","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0205","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5851","article-title":"MAPSeg: unified unsupervised domain adaptation for heterogeneous medical image segmentation based on 3d masked autoencoding and Pseudo-Labeling","author":"Zhang","year":"2024"},{"issue":"2","key":"10.1016\/j.asoc.2026.115314_bib0210","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1109\/JBHI.2022.3140853","article-title":"Margin preserving Self-Paced contrastive learning towards domain adaptation for medical image segmentation","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115314_bib0215","series-title":"IJCAI","first-page":"3071","article-title":"Enhancing unsupervised domain adaptation via semantic similarity constraint for medical image segmentation","author":"Hu","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102214","article-title":"S-CUDA: self-cleansing unsupervised domain adaptation for medical image segmentation","volume":"74","author":"Liu","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0225","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"2189","article-title":"Subtype-aware unsupervised domain adaptation for medical diagnosis","volume":"vol. 35","author":"Liu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115314_bib0230","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102969","article-title":"Learning with limited target data to detect cells in cross-modality images","volume":"90","author":"Xing","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0235","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"11621","article-title":"M3-UDA: a new benchmark for unsupervised domain adaptive fetal cardiac structure detection","author":"Pu","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0240","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"3225","article-title":"Correcting pseudo labels with label distribution for unsupervised domain adaptive vulnerable plaque detection","author":"Shi","year":"2021"},{"key":"10.1016\/j.asoc.2026.115314_bib0245","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part II 24","first-page":"549","article-title":"Adapting Off-the-Shelf source segmenter for target medical image segmentation","author":"Liu","year":"2021"},{"key":"10.1016\/j.asoc.2026.115314_bib0250","series-title":"Proceedings of the 30th ACM International Conference on Multimedia","first-page":"1935","article-title":"Alleviating style sensitivity then adapting: source-free domain adaptation for medical image segmentation","author":"Ye","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0255","author":"Gu"},{"issue":"5","key":"10.1016\/j.asoc.2026.115314_bib0260","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1109\/TMI.2021.3131245","article-title":"Domain adaptation meets Zero-Shot learning: an Annotation-Efficient approach to Multi-Modality medical image segmentation","volume":"41","author":"Bian","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0265","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"393","article-title":"LUCIDA: Low-Dose Universal-Tissue CT image domain adaptation for medical segmentation","author":"Chen","year":"2024"},{"issue":"10","key":"10.1016\/j.asoc.2026.115314_bib0270","doi-asserted-by":"crossref","first-page":"2926","DOI":"10.1109\/TMI.2021.3059265","article-title":"SELF-ATTENTIVE SPATIAL ADAPTIVE NORMALIZATION FOR CROSS-MODALITY DOMAIN ADAPTATION","volume":"40","author":"Tomar","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0275","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24","first-page":"201","article-title":"Semantic consistent unsupervised domain adaptation for Cross-Modality medical image segmentation","author":"Zeng","year":"2021"},{"key":"10.1016\/j.asoc.2026.115314_bib0280","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102037","article-title":"Super-Resolution of cardiac MR cine imaging using conditional GANs and unsupervised transfer learning","volume":"71","author":"Xia","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0285","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2018: 21st International Conference, Granada, Spain, September 16\u201320, 2018, Proceedings, Part II 11","first-page":"777","article-title":"Tumor-Aware, adversarial domain adaptation from CT to MRI for lung cancer segmentation","author":"Jiang","year":"2018"},{"key":"10.1016\/j.asoc.2026.115314_bib0290","series-title":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"UNSUPERVISED DOMAIN ADAPTATION FOR MEDICAL IMAGE SEGMENTATION WITH CONDITIONAL DIFFUSION MODEL","author":"Zhao","year":"2024"},{"issue":"10","key":"10.1016\/j.asoc.2026.115314_bib0295","doi-asserted-by":"crossref","first-page":"4976","DOI":"10.1109\/JBHI.2022.3162118","article-title":"A novel 3d unsupervised domain adaptation framework for cross-modality medical image segmentation","volume":"26","author":"Yao","year":"2022","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"8","key":"10.1016\/j.asoc.2026.115314_bib0300","doi-asserted-by":"crossref","first-page":"2133","DOI":"10.1109\/TMI.2023.3244252","article-title":"Deep-Learning-Based metal artefact reduction with unsupervised domain adaptation regularization for practical CT images","volume":"42","author":"Du","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0305","author":"Roca"},{"key":"10.1016\/j.asoc.2026.115314_bib0310","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103390","article-title":"Multi-Category fusion contrastive learning with core data selection for robust RGB image-based dental caries classification","author":"Zhang","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.asoc.2026.115314_bib0315","series-title":"Proceedings of the 2025 International Conference on Multimedia Retrieval","first-page":"2108","article-title":"Core Inter-Category contrastive learning for enhancing robustness of caries classification","author":"Zhang","year":"2025"},{"issue":"6","key":"10.1016\/j.asoc.2026.115314_bib0320","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TMI.2023.3238114","article-title":"Unsupervised Cross-Modality adaptation via dual Structural-Oriented guidance for 3d medical image segmentation","volume":"42","author":"Xian","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"10.1016\/j.asoc.2026.115314_bib0325","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1109\/TMI.2024.3525095","article-title":"Exploring contrastive pre-training for domain connections in medical image segmentation","volume":"44","author":"Zhang","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.106794","article-title":"Category-weight instance fusion learning for unsupervised domain adaptation on breast cancer histopathology images","volume":"99","author":"Zhang","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115314_bib0335","article-title":"Moment-consistent contrastive cyclegan for cross-domain pancreatic image segmentation","volume":"44","author":"Chen","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0340","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"11858","article-title":"Boosting novel category discovery over domains with soft contrastive learning and all in one classifier","author":"Zang","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0345","series-title":"Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 9","first-page":"143","article-title":"Semantic-Aware generative adversarial nets for unsupervised domain adaptation in chest x-ray segmentation","author":"Chen","year":"2018"},{"key":"10.1016\/j.asoc.2026.115314_bib0350","series-title":"2023 19th International Symposium on Medical Information Processing and Analysis (SIPAIM)","first-page":"1","article-title":"Unsupervised harmonization of brain MRI using 3d cyclegans and its effect on brain age prediction","author":"Komandur","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0355","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops","article-title":"Improving robustness of deep learning based knee MRI segmentation: mixup and adversarial domain adaptation","author":"Panfilov","year":"2019"},{"key":"10.1016\/j.asoc.2026.115314_bib0360","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"865","article-title":"Synergistic image and feature adaptation: towards Cross-Modality domain adaptation for medical image segmentation","volume":"vol. 33","author":"Chen","year":"2019"},{"key":"10.1016\/j.asoc.2026.115314_bib0365","series-title":"IJCAI","first-page":"3291","article-title":"Unsupervised domain adaptation with Dual-Scheme fusion network for medical image segmentation","author":"Zou","year":"2020"},{"key":"10.1016\/j.asoc.2026.115314_bib0370","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106530","article-title":"Ecsd-net: a joint optic disc and CUP segmentation and glaucoma classification network based on unsupervised domain adaptation","volume":"213","author":"Liu","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"issue":"2","key":"10.1016\/j.asoc.2026.115314_bib0375","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1109\/JBHI.2023.3336965","article-title":"ST-GAN: a swin Transformer-Based generative adversarial network for unsupervised domain adaptation of Cross-Modality cardiac segmentation","volume":"28","author":"Zhang","year":"2023","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.asoc.2026.115314_bib0380","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102924","article-title":"Generative appearance replay for continual unsupervised domain adaptation","volume":"89","author":"Chen","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0385","series-title":"2023 IEEE International Conference on Medical Artificial Intelligence (MedAI)","first-page":"101","article-title":"Nucleus detection based on adversarial domain adaptation with cross-domain consistency","author":"Guo","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0390","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.107275","article-title":"Diagnosis of atrial fibrillation based on unsupervised domain adaptation","volume":"164","author":"Du","year":"2023","journal-title":"Comput. Biol. Med."},{"issue":"39","key":"10.1016\/j.asoc.2026.115314_bib0395","doi-asserted-by":"crossref","first-page":"15540","DOI":"10.1021\/acs.analchem.4c01581","article-title":"MURDA: multisource unsupervised Raman spectroscopy domain adaptation model with reconstructed target domains for medical diagnosis assistance","volume":"96","author":"Liu","year":"2024","journal-title":"Anal. Chem."},{"key":"10.1016\/j.asoc.2026.115314_bib0400","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"623","article-title":"Unsupervised domain adaptation for medical image segmentation by selective entropy constraints and adaptive semantic alignment","volume":"vol. 37","author":"Feng","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108759","article-title":"Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation","volume":"178","author":"Liu","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115314_bib0410","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"745","article-title":"Attention-Enhanced disentangled representation learning for unsupervised domain adaptation in cardiac segmentation","author":"Sun","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0415","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106306","article-title":"CariesFG: a fine-grained RGB image classification framework with attention mechanism for dental caries","volume":"123","author":"Jiang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0420","article-title":"RDFNet: a fast caries detection method incorporating transformer mechanism","volume":"2021","author":"Jiang","year":"2021","journal-title":"Computational and Mathematical Methods in Medicine"},{"key":"10.1016\/j.asoc.2026.115314_bib0425","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2024.107159","article-title":"Unsupervised attention-guided domain adaptation model for acute lymphocytic leukemia (ALL) diagnosis","volume":"101","author":"Baydilli","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"issue":"11","key":"10.1016\/j.asoc.2026.115314_bib0430","doi-asserted-by":"crossref","first-page":"3445","DOI":"10.1109\/TMI.2022.3186698","article-title":"Dual adversarial attention mechanism for unsupervised domain adaptive medical image segmentation","volume":"41","author":"Chen","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.asoc.2026.115314_bib0435","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TRPMS.2024.3453401","article-title":"BTMuda: a bi-level multi-source unsupervised domain adaptation framework for breast cancer diagnosis","volume":"9","author":"Yang","year":"2025","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"issue":"7","key":"10.1016\/j.asoc.2026.115314_bib0440","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.1109\/TMI.2024.3367367","article-title":"A collaborative Self-Supervised domain adaptation for Low-Quality medical image enhancement","volume":"43","author":"Hou","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0445","article-title":"Source free domain adaptation for kidney and tumor image segmentation with wavelet style mining","volume":"14","author":"Yin","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115314_bib0450","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.108055","article-title":"Enhancing medical image analysis with unsupervised domain adaptation approach across microscopes and magnifications","volume":"170","author":"Ilyas","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115314_bib0455","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1501","article-title":"Arbitrary style transfer in real-time with adaptive instance normalization","author":"Huang","year":"2017"},{"key":"10.1016\/j.asoc.2026.115314_bib0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107722","article-title":"TIG-UDA: generative unsupervised domain adaptation with transformer-embedded invariance for cross-modality medical image segmentation","volume":"106","author":"Li","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115314_bib0465","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102651","article-title":"Tunable image quality control of 3-d ultrasound using switchable CycleGAN","volume":"83","author":"Huh","year":"2023","journal-title":"Med. Image Anal."},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0470","doi-asserted-by":"crossref","first-page":"3738","DOI":"10.1109\/TMI.2023.3306105","article-title":"Fvp: Fourier visual prompting for source-free unsupervised domain adaptation of medical image segmentation","volume":"42","author":"Wang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.asoc.2026.115314_bib0475","first-page":"12","article-title":"Reconstruction-driven dynamic refinement based unsupervised domain adaptation for joint optic disc and CUP segmentation","volume":"27","author":"Chen","year":"2023","journal-title":"Journal on Biomedical and Health Informatics (J-BHI)"},{"key":"10.1016\/j.asoc.2026.115314_bib0480","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"261","article-title":"OLVA: optimal latent vector alignment for unsupervised domain adaptation in medical image segmentation","author":"Al Chanti","year":"2021"},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0485","doi-asserted-by":"crossref","first-page":"3555","DOI":"10.1109\/TMI.2021.3090412","article-title":"Unsupervised domain adaptation with variational approximation for cardiac segmentation","volume":"40","author":"Wu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0490","series-title":"International Conference on Medical Imaging with Deep Learning","first-page":"1444","article-title":"Unsupervised domain adaptation through shape modeling for medical image segmentation","author":"Yao","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0495","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2021: 24th International Conference, Strasbourg, France, September 27\u2013October 1, 2021, Proceedings, Part III 24","first-page":"282","article-title":"Unsupervised domain adaptation for small bowel segmentation using disentangled representation","author":"Shin","year":"2021"},{"key":"10.1016\/j.asoc.2026.115314_bib0500","author":"Lin"},{"key":"10.1016\/j.asoc.2026.115314_bib0505","doi-asserted-by":"crossref","first-page":"4882","DOI":"10.1109\/TIP.2024.3451934","article-title":"Style consistency unsupervised domain adaptation medical image segmentation","volume":"33","author":"Chen","year":"2024","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0510","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TMI.2022.3192303","article-title":"Unsupervised domain adaptation for medical image segmentation by disentanglement learning and self-training","volume":"43","author":"Xie","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0515","series-title":"European Conference on Computer Vision","first-page":"735","article-title":"Unsupervised domain adaptation using feature disentanglement and GCNS for medical image classification","author":"Mahapatra","year":"2022"},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0520","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1109\/TMI.2024.3431192","article-title":"Unsupervised domain adaptation for EM image denoising with invertible networks","volume":"44","author":"Deng","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0525","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103440","article-title":"Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation","volume":"101","author":"Cai","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0530","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107573","article-title":"Style adaptation for avoiding semantic inconsistency in unsupervised domain adaptation medical image segmentation","volume":"105","author":"Liu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115314_bib0535","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"4023","article-title":"What can be transferred: unsupervised domain adaptation for endoscopic lesions segmentation","author":"Dong","year":"2020"},{"key":"10.1016\/j.asoc.2026.115314_bib0540","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.neucom.2021.04.104","article-title":"Inter-patient ECG arrhythmia heartbeat classification based on unsupervised domain adaptation","volume":"454","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115314_bib0545","series-title":"2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Fpl-uda: filtered pseudo label-based unsupervised cross-modality adaptation for vestibular schwannoma segmentation","author":"Wu","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0550","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2025.107763","article-title":"Enhancing collaboration between teacher and student for effective cross-domain nuclei detection and classification","volume":"106","author":"Wu","year":"2025","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.asoc.2026.115314_bib0555","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101766","article-title":"Uncertainty-aware multi-view co-training for semi-supervised medical image segmentation and domain adaptation","volume":"65","author":"Xia","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0560","series-title":"International Conference on Medical Imaging with Deep Learning","first-page":"1096","article-title":"Unsupervised domain adaptation for medical image segmentation via self-training of early features","author":"Sheikh","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0565","author":"Shin"},{"key":"10.1016\/j.asoc.2026.115314_bib0570","series-title":"2024 IEEE Conference on Artificial Intelligence (CAI)","first-page":"234","article-title":"Asymmetric Source-Free unsupervised domain adaptation for medical image diagnosis","author":"Zhang","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0575","series-title":"ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","first-page":"1","article-title":"A Deformable-Based Source-Free unsupervised domain adaptation method for cervical cell detection","author":"Pan","year":"2025"},{"key":"10.1016\/j.asoc.2026.115314_bib0580","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2025.110055","article-title":"Unsupervised domain adaptation with multi-level distillation boost and adaptive mask for medical image segmentation","volume":"190","author":"Wang","year":"2025","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.asoc.2026.115314_bib0585","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"7412","article-title":"Sdc-uda: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation","author":"Shin","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0590","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.110378","article-title":"O2m-uda: unsupervised dynamic domain adaptation for one-to-multiple medical image segmentation","volume":"265","author":"Jiang","year":"2023","journal-title":"Knowl.-based Syst."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0595","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00138-024-01615-2","article-title":"Unsupervised domain adaptation by cross-domain consistency learning for CT body composition","volume":"36","author":"Ali","year":"2025","journal-title":"Mach. Vis. Appl."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0600","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1038\/s41598-024-83018-x","article-title":"Unsupervised domain adaptation teacher\u2013student network for retinal vessel segmentation via fullresolution refined model","volume":"15","author":"Yue","year":"2025","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.asoc.2026.115314_bib0605","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1109\/TMI.2023.3335651","article-title":"Enhancing and adapting in the clinic: Source-Free unsupervised domain adaptation for medical image enhancement","volume":"43","author":"Li","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0610","series-title":"2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Adaptive entropy regularization for unsupervised domain adaptation in medical image segmentation","author":"Shi","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0615","series-title":"Proceedings of the 41st International Conference on Machine Learning","first-page":"41204","article-title":"Unsupervised domain adaptation for anatomical structure detection in ultrasound images","volume":"vol. 235","author":"Pu","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0620","series-title":"Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part I 23","first-page":"490","article-title":"Source-relaxed domain adaptation for image segmentation","author":"Bateson","year":"2020"},{"key":"10.1016\/j.asoc.2026.115314_bib0625","author":"Kondo"},{"key":"10.1016\/j.asoc.2026.115314_bib0630","series-title":"International Conference on Intelligent Computing","first-page":"168","article-title":"An unsupervised domain adaptive network based on category prototype alignment for medical image segmentation","author":"Yu","year":"2023"},{"issue":"6","key":"10.1016\/j.asoc.2026.115314_bib0635","doi-asserted-by":"crossref","first-page":"4299","DOI":"10.1002\/mp.17757","article-title":"Histogram matching-enhanced adversarial learning for unsupervised domain adaptation in medical image segmentation","volume":"52","author":"Qian","year":"2025","journal-title":"Med. Phys."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0640","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TMI.2022.3210133","article-title":"Domain and content adaptive convolution based multi-source domain generalization for medical image segmentation","volume":"42","author":"Hu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0645","doi-asserted-by":"crossref","first-page":"3604","DOI":"10.1109\/TMI.2021.3090432","article-title":"Structure-driven unsupervised domain adaptation for cross-modality cardiac segmentation","volume":"40","author":"Cui","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.asoc.2026.115314_bib0650","doi-asserted-by":"crossref","first-page":"2338","DOI":"10.1109\/TMI.2023.3247941","article-title":"Shape-Aware joint distribution alignment for Cross-Domain image segmentation","volume":"42","author":"Wang","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0655","first-page":"4071","article-title":"Psigan: joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation-based MRI segmentation","volume":"39","author":"Jiang","year":"2020","journal-title":"IEEE TMI"},{"key":"10.1016\/j.asoc.2026.115314_bib0660","article-title":"Egfda: experience-guided fine-grained domain adaptation for cross-domain pneumonia diagnosis","author":"Zhao","year":"2024","journal-title":"Knowl.-based Syst."},{"issue":"5","key":"10.1016\/j.asoc.2026.115314_bib0665","doi-asserted-by":"crossref","first-page":"8525","DOI":"10.1109\/TNNLS.2024.3409573","article-title":"Unsupervised domain adaptation for Low-Dose CT reconstruction via Bayesian uncertainty alignment","volume":"36","author":"Chen","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.asoc.2026.115314_bib0670","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102473","article-title":"Self-rule to multi-adapt: generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection","volume":"79","author":"Abbet","year":"2022","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0675","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"189","article-title":"D-master: mask annealed transformer for unsupervised domain adaptation in breast cancer detection from mammograms","author":"Ashraf","year":"2024"},{"issue":"7","key":"10.1016\/j.asoc.2026.115314_bib0680","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1109\/TMI.2020.2972701","article-title":"Unsupervised bidirectional Cross-Modality adaptation via deeply synergistic image and feature alignment for medical image segmentation","volume":"39","author":"Chen","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0685","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107055","article-title":"Unsupervised domain adaptive myocardial infarction MRI classification diagnostics model based on target domain confused sample resampling","volume":"226","author":"Xie","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.asoc.2026.115314_bib0690","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"11878","article-title":"Graphecho: graph-driven unsupervised domain adaptation for echocardiogram video segmentation","author":"Yang","year":"2023"},{"key":"10.1016\/j.asoc.2026.115314_bib0695","series-title":"International Conference on Neural Information Processing","first-page":"446","article-title":"Clusteruda: latent space clustering in unsupervised domain adaption for pulmonary nodule detection","author":"Wang","year":"2022"},{"key":"10.1016\/j.asoc.2026.115314_bib0700","series-title":"2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)","first-page":"1284","article-title":"Adversarial-based domain adaptation networks for unsupervised tumour detection in histopathology","author":"Figueira","year":"2020"},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0705","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TMI.2020.3023466","article-title":"Pdam: a panoptic-level feature alignment framework for unsupervised domain adaptive instance segmentation in microscopy images","volume":"40","author":"Liu","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0710","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102628","article-title":"Crossmoda 2021 challenge: benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation","volume":"83","author":"Dorent","year":"2023","journal-title":"Med. Image Anal."},{"issue":"10","key":"10.1016\/j.asoc.2026.115314_bib0715","doi-asserted-by":"crossref","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","article-title":"The multimodal brain tumor image segmentation benchmark (brats)","volume":"34","author":"Menze","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"18","key":"10.1016\/j.asoc.2026.115314_bib0720","doi-asserted-by":"crossref","first-page":"9078","DOI":"10.1073\/pnas.1900390116","article-title":"Reduced default mode network functional connectivity in patients with recurrent major depressive disorder","volume":"116","author":"Yan","year":"2019","journal-title":"Proc. Natl. Acad. Sci."},{"key":"10.1016\/j.asoc.2026.115314_bib0725","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"48","article-title":"Evaluation of retinal image quality assessment networks in different color-spaces","author":"Fu","year":"2019"},{"key":"10.1016\/j.asoc.2026.115314_bib0730","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115314_bib0735","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101570","article-title":"Refuge challenge: a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med. Image Anal."},{"issue":"11","key":"10.1016\/j.asoc.2026.115314_bib0740","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/TMI.2018.2837502","article-title":"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?","volume":"37","author":"Bernard","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0745","article-title":"Evaluation framework for algorithms segmenting short axis cardiac MRI","volume":"49","author":"Radau","year":"2009","journal-title":"The MIDAS Journal"},{"issue":"9","key":"10.1016\/j.asoc.2026.115314_bib0750","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","article-title":"Deep learning for segmentation using an open large-scale dataset in 2d echocardiography","volume":"38","author":"Leclerc","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.asoc.2026.115314_bib0755","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2022.102680","article-title":"The liver tumor segmentation benchmark (lits)","volume":"84","author":"Bilic","year":"2023","journal-title":"Med. Image Anal."},{"issue":"1","key":"10.1016\/j.asoc.2026.115314_bib0760","doi-asserted-by":"crossref","first-page":"4128","DOI":"10.1038\/s41467-022-30695-9","article-title":"The medical segmentation decathlon","volume":"13","author":"Antonelli","year":"2022","journal-title":"Nat. Commun."},{"key":"10.1016\/j.asoc.2026.115314_bib0765","first-page":"1","article-title":"Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge","volume":"42","author":"Arnaud Arindra Adiyoso","year":"2017","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.asoc.2026.115314_bib0770","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2097","article-title":"Chestx-ray8: hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases","author":"Wang","year":"2017"},{"issue":"22","key":"10.1016\/j.asoc.2026.115314_bib0775","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer","volume":"318","author":"Bejnordi","year":"2017","journal-title":"Jama"},{"issue":"12","key":"10.1016\/j.asoc.2026.115314_bib0780","doi-asserted-by":"crossref","first-page":"3413","DOI":"10.1109\/TMI.2021.3085712","article-title":"Monusac2020: a multi-organ nuclei segmentation and classification challenge","volume":"40","author":"Verma","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"10.1016\/j.asoc.2026.115314_bib0785","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2083","article-title":"Annotated high-throughput microscopy image sets for validation","volume":"9","author":"Ljosa","year":"2012","journal-title":"Nat. Methods"},{"key":"10.1016\/j.asoc.2026.115314_bib0790","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.neuroimage.2017.03.010","article-title":"Spinal cord grey matter segmentation challenge","volume":"152","author":"Prados","year":"2017","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.asoc.2026.115314_bib0795","doi-asserted-by":"crossref","DOI":"10.1088\/2057-1976\/ac8ffa","article-title":"Learning-based landmark detection in pelvis x-rays with attention mechanism: data from the osteoarthritis initiative","volume":"9","author":"Pei","year":"2023","journal-title":"Biomed. Phys. Eng. Express"},{"key":"10.1016\/j.asoc.2026.115314_bib0800","series-title":"2024 International Conference on Cyberworlds (CW)","first-page":"372","article-title":"Diffusion-driven cycle-consistent domain adaptation for cross-modality medical image segmentation","author":"Su","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0805","author":"Zhou"},{"key":"10.1016\/j.asoc.2026.115314_bib0810","author":"Jiang"},{"key":"10.1016\/j.asoc.2026.115314_bib0815","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"188","article-title":"Diffusion-based domain adaptation for medical image segmentation using stochastic step alignment","author":"Ji","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0820","series-title":"International Workshop on Simulation and Synthesis in Medical Imaging","first-page":"13","article-title":"Adaptdiff: cross-modality domain adaptation via weak conditional semantic diffusion for retinal vessel segmentation","author":"Hu","year":"2024"},{"key":"10.1016\/j.asoc.2026.115314_bib0825","author":"Gong"},{"issue":"4","key":"10.1016\/j.asoc.2026.115314_bib0830","doi-asserted-by":"crossref","first-page":"24410","DOI":"10.48084\/etasr.10892","article-title":"Contrastive boundary-aware learning for unsupervised cross-modality whole heart segmentation","volume":"15","author":"Kotte","year":"2025","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"10.1016\/j.asoc.2026.115314_bib0835","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.126921","article-title":"Source-free unsupervised domain adaptation: current research and future directions","volume":"564","author":"Zhang","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115314_bib0840","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106230","article-title":"Source-free unsupervised domain adaptation: a survey","author":"Fang","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.asoc.2026.115314_bib0845","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2023.127190","article-title":"Uncertainty-aware pseudo-label filtering for source-free unsupervised domain adaptation","volume":"575","author":"Chen","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.asoc.2026.115314_bib0850","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109155","article-title":"Source-free unsupervised domain adaptation for cross-modality abdominal multi-organ segmentation","volume":"250","author":"Hong","year":"2022","journal-title":"Knowl.-based Syst."},{"key":"10.1016\/j.asoc.2026.115314_bib0855","author":"Bommasani"},{"key":"10.1016\/j.asoc.2026.115314_bib0860","author":"Wu"},{"issue":"10","key":"10.1016\/j.asoc.2026.115314_bib0865","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1038\/s41591-024-03139-8","article-title":"Integrated image-based deep learning and language models for primary diabetes care","volume":"30","author":"Li","year":"2024","journal-title":"Nat. Med."},{"key":"10.1016\/j.asoc.2026.115314_bib0870","author":"Shirokikh"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626007623?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626007623?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T16:46:24Z","timestamp":1783183584000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626007623"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":174,"alternative-id":["S1568494626007623"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115314","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A survey on unsupervised domain adaptation in medical imaging: Methods, datasets, and future outlook","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115314","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 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":"115314"}}