{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T03:25:16Z","timestamp":1774581916092,"version":"3.50.1"},"reference-count":84,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202403"],"award-info":[{"award-number":["62202403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007156","name":"Innovation and Technology Commission","doi-asserted-by":"publisher","award":["MHP\/002\/22"],"award-info":[{"award-number":["MHP\/002\/22"]}],"id":[{"id":"10.13039\/501100007156","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Innovation Committee Fund","award":["SGDX20210823103201011"],"award-info":[{"award-number":["SGDX20210823103201011"]}]},{"DOI":"10.13039\/501100008100","name":"The Hong Kong University of Science and Technology (HKUST) 30 for 30 Research Initiative Scheme","doi-asserted-by":"publisher","award":["FS111"],"award-info":[{"award-number":["FS111"]}],"id":[{"id":"10.13039\/501100008100","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1109\/tmi.2025.3584967","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T13:39:06Z","timestamp":1751377146000},"page":"4931-4944","source":"Crossref","is-referenced-by-count":5,"title":["FedDAG: Federated Domain Adversarial Generation Toward Generalizable Medical Image Analysis"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5844-1285","authenticated-orcid":false,"given":"Haoxuan","family":"Che","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology University, Hong Kong, SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0941-6612","authenticated-orcid":false,"given":"Yifei","family":"Wu","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7512-3599","authenticated-orcid":false,"given":"Haibo","family":"Jin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, The Hong Kong University of Science and Technology University, Hong Kong, SAR, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9273-2847","authenticated-orcid":false,"given":"Yong","family":"Xia","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8400-3780","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Department of Chemical and Biological Engineering, Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1136\/bjo-2023-323871"},{"issue":"7","key":"ref2","article-title":"Using deep learning for assessing image-quality of 3D macular scans from spectral-domain optical coherence tomography","volume":"63","author":"Ziqi","year":"2022","journal-title":"Investigative Ophthalmol. Vis. Sci."},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-020-00323-1"},{"key":"ref4","volume-title":"The European Parliament and of the Council","year":"2016"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2025.3591185"},{"key":"ref6","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Proc. AISTATS","author":"McMahan"},{"key":"ref7","first-page":"11905","article-title":"Out-of-distribution generalization of federated learning via implicit invariant relationships","volume-title":"Proc. ICML","author":"Guo"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00599"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-023-02702-z"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43907-0_66"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00044"},{"key":"ref13","article-title":"Efficient federated domain translation","volume-title":"Proc. ICLR","author":"Zhou"},{"key":"ref14","first-page":"69309","article-title":"StableFDG: Style and attention based learning for federated domain generalization","volume-title":"Proc. NeurIPS","author":"Park"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3257566"},{"key":"ref16","first-page":"38831","article-title":"Fedsr: A simple and effective domain generalization method for federated learning","volume-title":"Proc. NeurIPS","author":"Nguyen"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.02020"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3234977"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00385"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3271851"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2023.3263072"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3251380"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00576"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3301036"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00447"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10447043"},{"key":"ref27","first-page":"33794","article-title":"Beyond the federation: Topology-aware federated learning for generalization to unseen clients","volume-title":"Proc. ICML","author":"Ma"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02576"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01955"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58517-4_33"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7003"},{"key":"ref32","first-page":"19448","article-title":"Adversarial teacher\u2013student representation learning for domain generalization","volume-title":"Proc. NeurIPS","volume":"34","author":"Yang"},{"key":"ref33","article-title":"Learning to augment via implicit differentiation for domain generalization","volume-title":"Proc. BMVC","author":"Wang"},{"key":"ref34","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. ICML","author":"Arjovsky"},{"key":"ref35","article-title":"A style-based generator architecture for generative adversarial networks","author":"Karras","year":"2018","journal-title":"arXiv:1812.04948"},{"key":"ref36","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume-title":"Proc. NeurIPS","author":"Ho"},{"key":"ref37","first-page":"8162","article-title":"Improved denoising diffusion probabilistic models","volume-title":"Proc. ICML","author":"Nichol"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01042"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3506575"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43904-9_42"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-16437-8_50"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3195549"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01898"},{"key":"ref44","article-title":"The effect of intrinsic dataset properties on generalization: Unraveling learning differences between natural and medical images","volume-title":"Proc. ICLR","author":"Konz"},{"key":"ref45","article-title":"DRAC: Diabetic retinopathy analysis challenge with ultra-wide optical coherence tomography angiography images","author":"Qian","year":"2023","journal-title":"arXiv:2304.02389"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2024.3451937"},{"key":"ref47","article-title":"What do we mean by generalization in federated learning?","volume-title":"Proc. ICLR","author":"Yuan"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.609"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01026"},{"key":"ref50","article-title":"Domain generalization via model-agnostic learning of semantic features","volume-title":"Proc. NeurIPS","author":"Dou"},{"key":"ref51","article-title":"Federated learning for generalization, robustness, fairness: A survey and benchmark","author":"Huang","year":"2023","journal-title":"arXiv:2311.06750"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3334213"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00777"},{"key":"ref54","article-title":"Stable diffusion-based data augmentation for federated learning with non-IID data","author":"Morafah","year":"2024","journal-title":"arXiv:2405.07925"},{"key":"ref55","first-page":"21111","article-title":"Virtual homogeneity learning: Defending against data heterogeneity in federated learning","volume-title":"Proc. 39th Int. Conf. Mach. Learn.","author":"Tang"},{"key":"ref56","article-title":"Federated adversarial domain adaptation","author":"Peng","year":"2019","journal-title":"arXiv:1911.02054"},{"key":"ref57","first-page":"16849","article-title":"Collaborative heterogeneous causal inference beyond meta-analysis","volume-title":"Proc. 41st Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref58","article-title":"Prompt public large language models to synthesize data for private on-device applications","author":"Wu","year":"2024","journal-title":"arXiv:2404.04360"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i3.28038"},{"key":"ref60","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. NeurIPS","author":"Tarvainen"},{"key":"ref61","article-title":"Sharpness-aware minimization for efficiently improving generalization","volume-title":"Proc. ICLR","author":"Foret"},{"key":"ref62","first-page":"31269","article-title":"Improving the model consistency of decentralized federated learning","volume-title":"Proc. 40th Int. Conf. Mach. Learn.","author":"Shi"},{"key":"ref63","article-title":"Locally estimated global perturbations are better than local perturbations for federated sharpness-aware minimization","author":"Fan","year":"2024","journal-title":"arXiv:2405.18890"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00240"},{"key":"ref65","first-page":"22405","article-title":"SWAD: Domain generalization by seeking flat minima","volume-title":"Proc. NeurIPS","author":"Cha"},{"key":"ref66","article-title":"FedBN: Federated learning on non-IID features via local batch normalization","volume-title":"Proc. ICLR","author":"Li"},{"key":"ref67","first-page":"429","article-title":"Federated optimization in heterogeneous networks","volume-title":"Proc. Mach. Learn.","author":"Li"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00990"},{"key":"ref69","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume-title":"Proc. NeurIPS","volume":"33","author":"Fallah"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_8"},{"key":"ref71","article-title":"Domain generalization with mixstyle","volume-title":"Proc. ICLR","author":"Zhou"},{"key":"ref72","first-page":"5637","article-title":"WILDS: A benchmark of in-the-Wild distribution shifts","volume-title":"Proc. ICML","author":"Koh"},{"key":"ref73","article-title":"Mitosis domain generalization challenge","volume-title":"Proc. MICCAI","author":"Aubreville"},{"key":"ref74","first-page":"5315","article-title":"FLamby: Datasets and benchmarks for cross-silo federated learning in realistic healthcare settings","volume-title":"Proc. NeurIPS","author":"Terrail"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2018.161"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363547"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-024-03387-w"},{"key":"ref78","article-title":"Loss surfaces, mode connectivity, and fast ensembling of DNNs","volume-title":"Proc. NeurIPS","author":"Garipov"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20050-2_38"},{"key":"ref80","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2021.3118354"},{"key":"ref81","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00052"},{"key":"ref82","first-page":"79748","article-title":"FedGCN: Convergence-communication tradeoffs in federated training of graph convolutional networks","volume-title":"Proc. NeurIPS","author":"Yao"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.591"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/42\/11279972\/11062124.pdf?arnumber=11062124","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T18:40:47Z","timestamp":1765219247000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11062124\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":84,"journal-issue":{"issue":"12"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2025.3584967","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]}}}