{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T17:01:44Z","timestamp":1775667704118,"version":"3.50.1"},"reference-count":60,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","license":[{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,7,1]],"date-time":"2023-07-01T00:00:00Z","timestamp":1688169600000},"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":["62102247"],"award-info":[{"award-number":["62102247"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1109\/tmi.2022.3221724","type":"journal-article","created":{"date-parts":[[2022,11,14]],"date-time":"2022-11-14T21:46:36Z","timestamp":1668462396000},"page":"1969-1981","source":"Crossref","is-referenced-by-count":41,"title":["A Federated Learning System for Histopathology Image Analysis With an Orchestral Stain-Normalization GAN"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7866-3339","authenticated-orcid":false,"given":"Yiqing","family":"Shen","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Arcot","family":"Sowmya","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}]},{"given":"Yulin","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"given":"Xiaoyao","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7934-5698","authenticated-orcid":false,"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, ShanghaiTech University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7459-257X","authenticated-orcid":false,"given":"Jing","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00133"},{"key":"ref57","article-title":"Think locally, act globally: Federated learning with local and global representations","author":"liang","year":"2020","journal-title":"arXiv 2001 01523"},{"key":"ref12","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref56","first-page":"1","article-title":"FEDBN: Federated learning on non-iid features via local batch normalization","author":"li","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref15","first-page":"2234","article-title":"Improved techniques for training GANs","volume":"29","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref59","article-title":"Personalized federated learning: A meta-learning approach","author":"fallah","year":"2020","journal-title":"arXiv 2002 07948"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1117\/12.2580688"},{"key":"ref58","article-title":"Federated learning with personalization layers","author":"arivazhagan","year":"2019","journal-title":"arXiv 1912 00818"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1080\/00220973.1993.9943832"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101544"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI48211.2021.9434078"},{"key":"ref55","first-page":"150","article-title":"Inverse distance aggregation for federated learning with non-iid data","author":"yeganeh","year":"2020","journal-title":"Domain Adaptation and Representation Transfer and Distributed and Collaborative Learning"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2017.7950492"},{"key":"ref54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5121\/ijdkp.2015.5201","article-title":"A review on evaluation metrics for data classification evaluations","volume":"5","author":"hossin","year":"2015","journal-title":"International Journal of Data Mining & Knowledge Management Process"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/38.946629"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87237-3_55"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2014.2303294"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"ref51","article-title":"MultiPathGAN: Structure preserving stain normalization using unsupervised multi-domain adversarial network with perception loss","author":"nazki","year":"2022","journal-title":"arXiv 2204 09782"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2016.05.003"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2011.2109730"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2529665"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2009.5193250"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1145\/3397391.3397442"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2018.2867350"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2476509"},{"key":"ref49","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1097\/PAI.0000000000000003"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00107"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-018-0177-5"},{"key":"ref4","first-page":"133","article-title":"Privacy-preserving federated brain tumour segmentation","author":"li","year":"2019","journal-title":"Proc Int Workshop Mach Learn Med Imag"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-69250-1"},{"key":"ref6","first-page":"92","article-title":"Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmentation","author":"sheller","year":"2018","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocy017"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002730"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2019.00095"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60636-7_1"},{"key":"ref37","article-title":"Tutorial on variational autoencoders","author":"doersch","year":"2016","journal-title":"arXiv 1606 05908"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759317"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2019.00193"},{"key":"ref33","first-page":"1","article-title":"Generative multi-adversarial networks","volume":"2017","author":"durugkar","year":"0","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref32","first-page":"234","article-title":"U-Net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput -Assist Intervent"},{"key":"ref2","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","author":"mcmahan","year":"2017","journal-title":"Artificial Intelligence and Statistics"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1799-6"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.3390\/ijms18010197"},{"key":"ref38","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"Proc 31st Int Conf Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759152"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref26","article-title":"Experiments of federated learning for COVID-19 chest X-ray images","author":"liu","year":"2020","journal-title":"arXiv 2007 05592"},{"key":"ref25","article-title":"Advances and open problems in federated learning","author":"kairouz","year":"2019","journal-title":"arXiv 1912 04977"},{"key":"ref20","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/MVIP49855.2020.9116895"},{"key":"ref21","article-title":"Neural stain-style transfer learning using GAN for histopathological images","author":"cho","year":"2017","journal-title":"arXiv 1710 08543"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-60548-3_13"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102298"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01243"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134012"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10168838\/09947057.pdf?arnumber=9947057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T17:43:02Z","timestamp":1689615782000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9947057\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7]]},"references-count":60,"journal-issue":{"issue":"7"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2022.3221724","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7]]}}}