{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T15:51:39Z","timestamp":1780588299349,"version":"3.54.1"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"9","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"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":["U22A2024"],"award-info":[{"award-number":["U22A2024"]}],"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":["62271328"],"award-info":[{"award-number":["62271328"]}],"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":["62071309"],"award-info":[{"award-number":["62071309"]}],"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":["62106153"],"award-info":[{"award-number":["62106153"]}],"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":["62172403"],"award-info":[{"award-number":["62172403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Distinguished Young Scholars Fund of Guangdong","award":["2021B1515020019"],"award-info":[{"award-number":["2021B1515020019"]}]},{"DOI":"10.13039\/501100003453","name":"National Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2019B1515120029"],"award-info":[{"award-number":["2019B1515120029"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"National Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2019A1515111205"],"award-info":[{"award-number":["2019A1515111205"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"National Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2020A1515110605"],"award-info":[{"award-number":["2020A1515110605"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Provincial Key Laboratory","award":["2020B121201001"],"award-info":[{"award-number":["2020B121201001"]}]},{"name":"Shenzhen Key Basic Research Project","award":["JCYJ20220818095809021"],"award-info":[{"award-number":["JCYJ20220818095809021"]}]},{"name":"Shenzhen Key Basic Research Project","award":["SGDX20201103095802007"],"award-info":[{"award-number":["SGDX20201103095802007"]}]},{"name":"Shenzhen Key Basic Research Project","award":["KCXFZ20201221173213036"],"award-info":[{"award-number":["KCXFZ20201221173213036"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Med. Imaging"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1109\/tmi.2023.3263216","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T19:12:52Z","timestamp":1680117172000},"page":"2714-2725","source":"Crossref","is-referenced-by-count":23,"title":["Fundus Image-Label Pairs Synthesis and Retinopathy Screening via GANs With Class-Imbalanced Semi-Supervised Learning"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8065-4941","authenticated-orcid":false,"given":"Yingpeng","family":"Xie","sequence":"first","affiliation":[{"name":"National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9813-4215","authenticated-orcid":false,"given":"Qiwei","family":"Wan","sequence":"additional","affiliation":[{"name":"National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6258-9787","authenticated-orcid":false,"given":"Hai","family":"Xie","sequence":"additional","affiliation":[{"name":"National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1779-931X","authenticated-orcid":false,"given":"Yanwu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Future Technology, South China University of Technology, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1248-1214","authenticated-orcid":false,"given":"Tianfu","family":"Wang","sequence":"additional","affiliation":[{"name":"National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1119-320X","authenticated-orcid":false,"given":"Shuqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3087-2550","authenticated-orcid":false,"given":"Baiying","family":"Lei","sequence":"additional","affiliation":[{"name":"National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Marshall Laboratory of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00400"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"},{"key":"ref56","first-page":"1","article-title":"Energy-based generative adversarial networks","author":"zhao","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref15","first-page":"1567","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","author":"cao","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref59","first-page":"5234","article-title":"Assessing generative models via precision and recall","author":"sajjadi","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00949"},{"key":"ref58","first-page":"7559","article-title":"Differentiable augmentation for data-efficient GAN training","author":"zhao","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref53","article-title":"REFUGE2 challenge: A treasure trove for multi-dimension analysis and evaluation in glaucoma screening","author":"fang","year":"2022","journal-title":"arXiv 2202 08994"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2022.3172773"},{"key":"ref11","first-page":"14567","article-title":"Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning","volume":"2020","author":"kim","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref55","first-page":"177","article-title":"A benchmark of ocular disease intelligent recognition: One shot for multi-disease detection","author":"li","year":"2020","journal-title":"Proc Int Symp Benchmarking Measuring Optim"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"ref54","article-title":"GAMMA challenge: Glaucoma grAding from multi-modality imAges","author":"wu","year":"2022","journal-title":"arXiv 2202 06511"},{"key":"ref17","article-title":"Class-imbalanced semi-supervised learning","author":"hyun","year":"2020","journal-title":"arXiv 2002 06815"},{"key":"ref16","first-page":"1","article-title":"Long-tail learning via logit adjustment","author":"menon","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2759102"},{"key":"ref18","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref51","article-title":"PALM: PAthoLogic myopia challenge","author":"fu","year":"2019","journal-title":"IEEE Dataport"},{"key":"ref50","first-page":"8082","article-title":"Class-imbalanced semi-supervised learning with adaptive thresholding","author":"guo","year":"2022","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.09.030"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-87240-3_31"},{"key":"ref48","first-page":"11828","article-title":"Smoothed adaptive weighting for imbalanced semi-supervised learning: Improve reliability against unknown distribution data","author":"lai","year":"2022","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106628"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01424"},{"key":"ref41","first-page":"896","article-title":"Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks","volume":"3","author":"lee","year":"2013","journal-title":"Proc Workshop Challenges Represent Learn (ICML)"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3028960"},{"key":"ref43","first-page":"1","article-title":"Decoupling representation and classifier for long-tailed recognition","author":"kang","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00956"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref7","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 Adv Neural Inf Process Syst"},{"key":"ref9","first-page":"596","article-title":"FixMatch: Simplifying semi-supervised learning with consistency and confidence","author":"sohn","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2018.07.004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/SAI.2014.6918213"},{"key":"ref6","first-page":"1","article-title":"Temporal ensembling for semi-supervised learning","author":"laine","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3031898"},{"key":"ref40","first-page":"529","article-title":"Semi-supervised learning by entropy minimization","author":"grandvalet","year":"2004","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","first-page":"3581","article-title":"A large-scale study on regularization and normalization in GANs","author":"kurach","year":"2019","journal-title":"Int Conf Mach Learn"},{"key":"ref34","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","author":"heusel","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref37","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref36","first-page":"6597","article-title":"Modulating early visual processing by language","author":"de vries","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3052243"},{"key":"ref33","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref32","first-page":"1","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.101570"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(17)30293-0"},{"key":"ref39","first-page":"1","article-title":"cGANs with projection discriminator","author":"miyato","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref38","first-page":"23505","article-title":"Rebooting ACGAN: Auxiliary classifier GANs with stable training","author":"kang","year":"2021","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3127558"},{"key":"ref23","first-page":"2234","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-020-01096-z"},{"key":"ref25","first-page":"6510","article-title":"Good semi-supervised learning that requires a bad GAN","author":"dai","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.07.001"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101874"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2020.3045475"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2903434"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2020.2981637"},{"key":"ref29","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref61","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","author":"springenberg","year":"2015","journal-title":"Proc Int Conf Learn Represent"}],"container-title":["IEEE Transactions on Medical Imaging"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/42\/10236920\/10087263.pdf?arnumber=10087263","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T18:20:31Z","timestamp":1695666031000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10087263\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":61,"journal-issue":{"issue":"9"},"URL":"https:\/\/doi.org\/10.1109\/tmi.2023.3263216","relation":{},"ISSN":["0278-0062","1558-254X"],"issn-type":[{"value":"0278-0062","type":"print"},{"value":"1558-254X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}