{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,28]],"date-time":"2026-06-28T10:28:54Z","timestamp":1782642534177,"version":"3.54.5"},"reference-count":79,"publisher":"Association for Research in Vision and Ophthalmology (ARVO)","issue":"11","license":[{"start":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T00:00:00Z","timestamp":1763683200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Trans. Vis. Sci. Tech."],"published-print":{"date-parts":[[2025,11,21]]},"DOI":"10.1167\/tvst.14.11.36","type":"journal-article","created":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T13:30:44Z","timestamp":1763731844000},"page":"36","source":"Crossref","is-referenced-by-count":1,"title":["Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning"],"prefix":"10.1167","volume":"14","author":[{"given":"Iyad","family":"Majid","sequence":"first","affiliation":[{"name":"Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nazlee","family":"Zebardast","sequence":"additional","affiliation":[{"name":"Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"27","reference":[{"issue":"18","key":"bib1","doi-asserted-by":"publisher","first-page":"1901","DOI":"10.1001\/jama.2014.3192","article-title":"The pathophysiology and treatment of glaucoma: a review","volume":"311","author":"Weinreb","year":"2014","journal-title":"JAMA"},{"issue":"1","key":"bib2","doi-asserted-by":"publisher","first-page":"e053805","DOI":"10.1136\/bmjopen-2021-053805","article-title":"Time trends, associations and prevalence of blindness and vision loss due to glaucoma: an analysis of observational data from the Global Burden of Disease Study 2017","volume":"12","author":"Sun","year":"2022","journal-title":"BMJ Open"},{"issue":"7","key":"bib3","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1093\/ajhp\/62.7.691","article-title":"Glaucoma and its treatment: a review","volume":"62","author":"Lee","year":"2005","journal-title":"Am J Health Syst Pharm"},{"issue":"1","key":"bib4","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.mjafi.2015.12.001","article-title":"Glaucoma: the \u201cBlack hole\u201d of irreversible blindness","volume":"72","author":"Parihar","year":"2016","journal-title":"Med J Armed Forces India"},{"issue":"3","key":"bib5","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1136\/bjo.2005.081224","article-title":"The number of people with glaucoma worldwide in 2010 and 2020","volume":"90","author":"Quigley","year":"2006","journal-title":"Br J Ophthalmol"},{"issue":"3","key":"bib6","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1001\/jama.1991.03470030069026","article-title":"Racial variations in the prevalence of primary open-angle glaucoma. The Baltimore Eye Survey","volume":"266","author":"Tielsch","year":"1991","journal-title":"JAMA"},{"issue":"6","key":"bib7","doi-asserted-by":"publisher","first-page":"1031","DOI":"10.1016\/j.ophtha.2010.10.024","article-title":"Differences in rates of glaucoma among Asian Americans and other racial groups, and among various Asian ethnic groups","volume":"118","author":"Stein","year":"2011","journal-title":"Ophthalmology"},{"issue":"4","key":"bib8","doi-asserted-by":"publisher","first-page":"394","DOI":"10.3109\/08820538.2016.1154165","article-title":"Glaucoma disparities in the Hispanic population","volume":"31","author":"Nathan","year":"2016","journal-title":"Semin Ophthalmol"},{"issue":"9","key":"bib9","doi-asserted-by":"publisher","first-page":"1198","DOI":"10.1001\/jamaophthalmol.2013.4694","article-title":"Socioeconomic disparity in use of eye care services among US adults with age-related eye diseases: National Health Interview Survey, 2002 and 2008","volume":"131","author":"Zhang","year":"2013","journal-title":"JAMA Ophthalmol"},{"issue":"4","key":"bib10","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1001\/jamaophthalmol.2017.6836","article-title":"Detection of glaucoma progression in individuals of African descent compared with those of European descent","volume":"136","author":"Gracitelli","year":"2018","journal-title":"JAMA Ophthalmol"},{"issue":"8","key":"bib11","first-page":"1115","article-title":"Longitudinal visual field variability and the ability to detect glaucoma progression in black and white individuals","volume":"106","author":"Stagg","year":"2022","journal-title":"Br J Ophthalmol"},{"issue":"12","key":"bib12","doi-asserted-by":"publisher","first-page":"1579","DOI":"10.1001\/archophthalmol.2012.1325","article-title":"Racial disparities in the use of ancillary testing to evaluate individuals with open-angle glaucoma","volume":"130","author":"Stein","year":"2012","journal-title":"Arch Ophthalmol"},{"issue":"7","key":"bib13","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1001\/archophthalmol.2011.163","article-title":"Racial and ethnic disparities in adherence to glaucoma follow-up visits in a county hospital population","volume":"129","author":"Murakami","year":"2011","journal-title":"Arch Ophthalmol"},{"key":"bib14","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ajo.2023.03.017","article-title":"The impact of social determinants of health on vision loss from cataracts and cataract surgery utilization in the United States - A National Health Interview Survey analysis","volume":"254","author":"Awidi","year":"2023","journal-title":"Am J Ophthalmol"},{"issue":"4","key":"bib15","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.ajo.2011.06.004","article-title":"An assessment of the health and economic burdens of glaucoma","volume":"152","author":"Varma","year":"2011","journal-title":"Am J Ophthalmol"},{"issue":"4","key":"bib16","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1001\/jamaophthalmol.2022.0055","article-title":"Racial and ethnic disparities in cost-related barriers to medication adherence among patients with glaucoma enrolled in the National Institutes of Health All of Us Research Program","volume":"140","author":"Delavar","year":"2022","journal-title":"JAMA Ophthalmol"},{"issue":"10","key":"bib17","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1167\/tvst.12.10.18","article-title":"Identifying, understanding, and addressing disparities in glaucoma care in the United States","volume":"12","author":"Davuluru","year":"2023","journal-title":"Transl Vis Sci Technol"},{"issue":"12","key":"bib18","doi-asserted-by":"publisher","first-page":"2398","DOI":"10.1016\/j.ophtha.2011.05.013","article-title":"The relationship between glaucoma medication adherence, eye drop technique, and visual field defect severity","volume":"118","author":"Sleath","year":"2011","journal-title":"Ophthalmology"},{"issue":"4","key":"bib19","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.ogla.2024.03.008","article-title":"The impact of social vulnerability on structural and functional glaucoma severity, worsening, and variability","volume":"7","author":"Almidani","year":"2024","journal-title":"Ophthalmol Glaucoma"},{"issue":"10","key":"bib20","doi-asserted-by":"publisher","first-page":"1738","DOI":"10.3390\/diagnostics13101738","article-title":"Automatic diagnosis of glaucoma from retinal images using deep learning approach","volume":"13","author":"Shoukat","year":"2023","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"bib21","doi-asserted-by":"publisher","first-page":"8064","DOI":"10.1038\/s41598-022-12147-y","article-title":"Glaucoma diagnosis using multi-feature analysis and a deep learning technique","volume":"12","author":"Akter","year":"2022","journal-title":"Sci Rep"},{"key":"bib22","doi-asserted-by":"publisher","first-page":"1157016","DOI":"10.3389\/fmed.2023.1157016","article-title":"Predicting near-term glaucoma progression: An artificial intelligence approach using clinical free-text notes and data from electronic health records","volume":"10","author":"Jalamangala Shivananjaiah","year":"2023","journal-title":"Front Med (Lausanne)"},{"issue":"11","key":"bib23","doi-asserted-by":"publisher","first-page":"2894","DOI":"10.3390\/diagnostics12112894","article-title":"Deep learning-based glaucoma screening using regional RNFL thickness in fundus photography","volume":"12","author":"Yang","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"4","key":"bib24","doi-asserted-by":"publisher","first-page":"718","DOI":"10.3390\/jcm10040718","article-title":"Recent advances and clinical application of color scanning laser ophthalmoscope","volume":"10","author":"Terasaki","year":"2021","journal-title":"J Clin Med"},{"issue":"11","key":"bib25","doi-asserted-by":"publisher","first-page":"2929","DOI":"10.1038\/s41591-023-02608-w","article-title":"The value of standards for health datasets in artificial intelligence-based applications","volume":"29","author":"Arora","year":"2023","journal-title":"Nat Med"},{"issue":"1","key":"bib27","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1186\/s12938-019-0682-x","article-title":"Retinal image synthesis from multiple-landmarks input with generative adversarial networks","volume":"18","author":"Yu","year":"2019","journal-title":"Biomed Eng Online"},{"issue":"3","key":"bib30","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1001\/jamainternmed.2018.7117","article-title":"Deep learning in medicine\u2014promise, progress, and challenges","volume":"179","author":"Wang","year":"2019","journal-title":"JAMA Intern Med"},{"issue":"5","key":"bib31","doi-asserted-by":"publisher","first-page":"2425","DOI":"10.1167\/iovs.10-6312","article-title":"Optical coherence tomography: history, current status, and laboratory work","volume":"52","author":"Gabriele","year":"2011","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"bib33","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.media.2018.07.001","article-title":"Synthesizing retinal and neuronal images with generative adversarial nets","volume":"49","author":"Zhao","year":"2018","journal-title":"Med Image Anal"},{"issue":"5","key":"bib35","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: a brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav Ther"},{"issue":"1","key":"bib36","doi-asserted-by":"publisher","first-page":"911","DOI":"10.12785\/ijcds\/130172","article-title":"An unsupervised machine learning algorithms: comprehensive review","volume":"13","author":"Naeem","year":"2023","journal-title":"Int J Comput Digit Syst"},{"key":"bib37","doi-asserted-by":"publisher","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":"103444","key":"bib38","doi-asserted-by":"crossref","first-page":"103444","DOI":"10.1016\/j.bspc.2021.103444","article-title":"Medical image registration using unsupervised deep neural network: A scoping literature review","volume":"73","author":"Abbasi","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"bib39","doi-asserted-by":"publisher","first-page":"67302","DOI":"10.1109\/ACCESS.2021.3074458","article-title":"Automated microaneurysms detection in retinal images using radon transform and supervised learning: application to mass screening of diabetic retinopathy","volume":"9","author":"Tavakoli","year":"2021","journal-title":"IEEE Access"},{"issue":"2","key":"bib40","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ophtha.2022.09.014","article-title":"Training deep learning models to work on multiple devices by cross-domain learning with no additional annotations","volume":"130","author":"Wu","year":"2023","journal-title":"Ophthalmology"},{"issue":"1","key":"bib41","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1364\/BOE.379978","article-title":"Reducing image variability across OCT devices with unsupervised unpaired learning for improved segmentation of retina","volume":"11","author":"Romo-Bucheli","year":"2020","journal-title":"Biomed Opt Express"},{"issue":"7","key":"bib43","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1167\/tvst.10.7.21","article-title":"Pix2pix conditional generative adversarial networks for Scheimpflug camera color-coded corneal tomography image generation","volume":"10","author":"Abdelmotaal","year":"2021","journal-title":"Transl Vis Sci Technol"},{"issue":"103628","key":"bib44","doi-asserted-by":"crossref","first-page":"103628","DOI":"10.1016\/j.compbiomed.2020.103628","article-title":"A generative adversarial network approach to predicting postoperative appearance after orbital decompression surgery for thyroid eye disease","volume":"118","author":"Yoo","year":"2020","journal-title":"Comput Biol Med"},{"issue":"1","key":"bib45","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/JBHI.2020.3045475","article-title":"DR-GAN: Conditional generative adversarial network for fine-grained lesion synthesis on diabetic retinopathy images","volume":"26","author":"Zhou","year":"2022","journal-title":"IEEE J Biomed Health Inform"},{"issue":"2","key":"bib46","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1167\/tvst.9.2.23","article-title":"DeshadowGAN: a deep learning approach to remove shadows from optical coherence tomography images","volume":"9","author":"Cheong","year":"2020","journal-title":"Transl Vis Sci Technol"},{"key":"bib47","doi-asserted-by":"publisher","first-page":"101906","DOI":"10.1016\/j.media.2020.101906","article-title":"Improving statistical power of glaucoma clinical trials using an ensemble of cyclical generative adversarial networks","volume":"68","author":"Lazaridis","year":"2021","journal-title":"Med Image Anal"},{"issue":"3","key":"bib50","article-title":"Optical coherence tomography image enhancement and layer detection using cycle-GAN","volume":"15","author":"Kim","year":"2025","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"bib51","doi-asserted-by":"publisher","first-page":"21580","DOI":"10.1038\/s41598-020-78696-2","article-title":"A novel deep learning conditional generative adversarial network for producing angiography images from retinal fundus photographs","volume":"10","author":"Tavakkoli","year":"2020","journal-title":"Sci Rep"},{"issue":"3","key":"bib52","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1109\/TMI.2017.2759102","article-title":"End-to-end adversarial retinal image synthesis","volume":"37","author":"Costa","year":"2018","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"bib53","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1109\/TMI.2021.3056395","article-title":"Leveraging regular fundus images for training UWF fundus diagnosis models via adversarial learning and pseudo-labeling","volume":"40","author":"Ju","year":"2021","journal-title":"IEEE Trans Med Imaging"},{"issue":"6","key":"bib54","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1167\/iovs.18-25839","article-title":"Optic disc tilt and glaucoma progression in myopic glaucoma: A longitudinal match-pair case-control study","volume":"60","author":"Seol","year":"2019","journal-title":"Invest Ophthalmol Vis Sci"},{"issue":"4","key":"bib55","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1016\/j.ophtha.2005.12.018","article-title":"The relationship between recurrent optic disc hemorrhage and glaucoma progression","volume":"113","author":"Kim","year":"2006","journal-title":"Ophthalmology"},{"issue":"2","key":"bib56","doi-asserted-by":"publisher","first-page":"127","DOI":"10.3341\/kjo.2016.30.2.127","article-title":"Clinical characteristics of juvenile-onset open angle glaucoma","volume":"30","author":"Kwun","year":"2016","journal-title":"Korean J Ophthalmol"},{"issue":"5","key":"bib57","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1097\/IJG.0b013e3181845ffd","article-title":"Influence of glaucomatous damage and optic disc size on glaucoma detection by scanning laser tomography","volume":"18","author":"Hoesl","year":"2009","journal-title":"J Glaucoma"},{"issue":"1","key":"bib59","doi-asserted-by":"publisher","first-page":"e51","DOI":"10.1016\/S2589-7500(20)30240-5","article-title":"A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability","volume":"3","author":"Khan","year":"2021","journal-title":"Lancet Digit Health"},{"issue":"1","key":"bib60","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1186\/s12886-021-01992-6","article-title":"Generalisability through local validation: overcoming barriers due to data disparity in healthcare","volume":"21","author":"Mitchell","year":"2021","journal-title":"BMC Ophthalmol"},{"issue":"6","key":"bib61","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1038\/s41551-021-00751-8","article-title":"Synthetic data in machine learning for medicine and healthcare","volume":"5","author":"Chen","year":"2021","journal-title":"Nat Biomed Eng"},{"issue":"32","key":"bib62","doi-asserted-by":"publisher","first-page":"eabq6147","DOI":"10.1126\/sciadv.abq6147","article-title":"Disparities in dermatology AI performance on a diverse, curated clinical image set","volume":"8","author":"Daneshjou","year":"2022","journal-title":"Sci Adv"},{"issue":"1","key":"bib63","doi-asserted-by":"publisher","first-page":"4750","DOI":"10.1038\/s41467-024-48972-0","article-title":"Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism","volume":"15","author":"Tan","year":"2024","journal-title":"Nat Commun"},{"issue":"4","key":"bib64","doi-asserted-by":"publisher","first-page":"100401","DOI":"10.1016\/j.xops.2023.100401","article-title":"Translation of color fundus photography into fluorescein angiography using deep learning for enhanced diabetic retinopathy screening","volume":"3","author":"Shi","year":"2023","journal-title":"Ophthalmol Sci"},{"issue":"4","key":"bib66","doi-asserted-by":"publisher","first-page":"100493","DOI":"10.1016\/j.xops.2024.100493","article-title":"FA4SANS-GAN: A novel machine learning generative adversarial network to further understand ophthalmic changes in spaceflight associated neuro-ocular syndrome (SANS)","volume":"4","author":"Kamran","year":"2024","journal-title":"Ophthalmol Sci"},{"issue":"1","key":"bib67","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s41746-024-01018-7","article-title":"Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening","volume":"7","author":"Chen","year":"2024","journal-title":"NPJ Digit Med"},{"key":"bib68","doi-asserted-by":"publisher","first-page":"105101","DOI":"10.1016\/j.cmpb.2019.105101","article-title":"Geographic atrophy segmentation in SD-OCT images using synthesized fundus autofluorescence imaging","volume":"182","author":"Wu","year":"2019","journal-title":"Comput Methods Programs Biomed"},{"issue":"12","key":"bib69","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1136\/bjophthalmol-2019-315338","article-title":"Prediction of OCT images of short-term response to anti-VEGF treatment for neovascular age-related macular degeneration using generative adversarial network","volume":"104","author":"Liu","year":"2020","journal-title":"Br J Ophthalmol"},{"issue":"8","key":"bib70","first-page":"1631","article-title":"CycleGAN-based deep learning technique for artifact reduction in fundus photography","volume":"258","author":"Yoo","year":"2020","journal-title":"Arbeitsphysiologie"},{"issue":"10","key":"bib71","doi-asserted-by":"publisher","first-page":"e0239913","DOI":"10.1371\/journal.pone.0239913","article-title":"Deep-learning-based enhanced optic-disc photography","volume":"15","author":"Ha","year":"2020","journal-title":"PLoS One"},{"issue":"6","key":"bib72","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1167\/tvst.10.6.33","article-title":"Generative adversarial network based automatic segmentation of corneal subbasal nerves on in vivo confocal microscopy images","volume":"10","author":"Yildiz","year":"2021","journal-title":"Transl Vis Sci Technol"},{"issue":"4","key":"bib74","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1007\/s42452-024-05871-9","article-title":"A feasibility study on the adoption of a generative denoising diffusion model for the synthesis of fundus photographs using a small dataset","volume":"6","author":"Kim","year":"2024","journal-title":"Discov Appl Sci"},{"issue":"10","key":"bib75","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.1136\/bjo-2024-325458","article-title":"Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges","volume":"108","author":"Sonmez","year":"2024","journal-title":"Br J Ophthalmol"},{"issue":"1","key":"bib76","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J Big Data"},{"issue":"1","key":"bib77","doi-asserted-by":"publisher","first-page":"12867","DOI":"10.1038\/s41598-022-16861-5","article-title":"Lung cancer CT image generation from a free-form sketch using style-based pix2pix for data augmentation","volume":"12","author":"Toda","year":"2022","journal-title":"Sci Rep"},{"issue":"1","key":"bib79","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s42256-019-0137-x","article-title":"Assessing the importance of magnetic resonance contrasts using collaborative generative adversarial networks","volume":"2","author":"Lee","year":"2020","journal-title":"Nat Mach Intell"},{"key":"bib26","doi-asserted-by":"crossref","DOI":"10.1109\/ICCV.2017.97","article-title":"Revisiting unreasonable effectiveness of data in deep learning era","volume-title":"2017 IEEE International Conference on Computer Vision (ICCV)","author":"Sun","year":"2017"},{"key":"bib28","first-page":"3","article-title":"When training and test sets are different: characterizing learning transfer","volume-title":"Dataset Shift in Machine Learning","author":"Qui\u00f1onero-Candela","year":"2009"},{"key":"bib29","article-title":"Exploring generalization in deep learning","author":"Neyshabur"},{"key":"bib32","doi-asserted-by":"crossref","article-title":"Generative adversarial networks","author":"Goodfellow","DOI":"10.1145\/3422622"},{"key":"bib34","doi-asserted-by":"crossref","article-title":"Unpaired image-to-image translation using cycle-consistent adversarial networks","author":"Zhu","DOI":"10.1109\/ICCV.2017.244"},{"key":"bib42","doi-asserted-by":"crossref","DOI":"10.1109\/DICTA52665.2021.9647266","article-title":"OCT retinal image-to-image translation: analysing the use of CycleGAN to improve retinal boundary semantic segmentation","volume-title":"2021 Digital Image Computing: Techniques and Applications (DICTA)","author":"Viedma","year":"2021"},{"key":"bib48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/ISBI56570.2024.10635123","article-title":"OCT2Confocal: 3D CycleGAN based translation of retinal OCT images to confocal microscopy","volume-title":"2024 IEEE International Symposium on Biomedical Imaging (ISBI)","author":"Tian","year":"2024"},{"key":"bib49","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1109\/ISBI.2019.8759158","article-title":"Using cyclegans for effectively reducing image variability across OCT devices and improving retinal fluid segmentation","volume-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","author":"Seebock","year":"2019"},{"key":"bib58","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan"},{"key":"bib65","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/ICUFN57995.2023.10200484","article-title":"GAN-based image-to-image translation of fundus photography: Topcon to eidon","volume-title":"2023 Fourteenth International Conference on Ubiquitous and Future Networks (ICUFN)","author":"Joo","year":"2023"},{"key":"bib73","article-title":"Generating realistic counterfactuals for retinal fundus and OCT images using diffusion models","author":"Ilanchezian"},{"key":"bib78","doi-asserted-by":"crossref","DOI":"10.1109\/CVPR.2018.00916","article-title":"StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation","volume-title":"2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Choi","year":"2018"}],"container-title":["Translational Vision Science & Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2811141","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T13:30:47Z","timestamp":1763731847000},"score":1,"resource":{"primary":{"URL":"https:\/\/tvst.arvojournals.org\/article.aspx?articleid=2811141"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":79,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2025,11,3]]}},"URL":"https:\/\/doi.org\/10.1167\/tvst.14.11.36","relation":{},"ISSN":["2164-2591"],"issn-type":[{"value":"2164-2591","type":"print"}],"subject":[],"published":{"date-parts":[[2025,11,21]]}}}