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V., Prabhu, J., Sandhiya, M., Praveenkumar, R., & Kumar, V. S. (2022). COVID-19 detection based on lung CT scan using deep learning techniques. Computational and Mathematical Methods in Medicine, 2022, Article 4698079.","DOI":"10.1155\/2022\/7672196"},{"issue":"23","key":"10.1016\/j.eswa.2026.131162_b0200","doi-asserted-by":"crossref","first-page":"e7211","DOI":"10.1002\/cpe.7211","article-title":"Jaya-tunicate swarm algorithm-based generative adversarial network for COVID-19 prediction with chest computed tomography images","volume":"34","author":"Doraiswami","year":"2022","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"10.1016\/j.eswa.2026.131162_b0205","article-title":"Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning","volume":"137","author":"Ghassemi","year":"2023","journal-title":"Applied Soft Computing"},{"issue":"2","key":"10.1016\/j.eswa.2026.131162_b0210","doi-asserted-by":"crossref","first-page":"87","DOI":"10.21037\/atm-21-4056","article-title":"Detecting brain lesions in suspected acute ischemic stroke with CT-based synthetic MRI using generative adversarial networks","volume":"10","author":"Hu","year":"2022","journal-title":"Annals of Translational Medicine"},{"issue":"3","key":"10.1016\/j.eswa.2026.131162_b0215","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1007\/s11227-022-04775-y","article-title":"How much BiGAN- and CycleGAN-learned hidden features are effective for COVID-19 detection from CT images? 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A two-stage generative model with CycleGAN and joint diffusion for MRI-based brain tumor detection. arXiv preprint arXiv:2311.03074."},{"issue":"2","key":"10.1016\/j.eswa.2026.131162_b0340","doi-asserted-by":"crossref","first-page":"223","DOI":"10.3390\/biomedicines10020223","article-title":"Brain tumor classification using a combination of variational autoencoders and generative adversarial networks","volume":"10","author":"Ahmad","year":"2022","journal-title":"Biomedicines"},{"key":"10.1016\/j.eswa.2026.131162_b0345","article-title":"Brain tumor classification using a pre-trained auxiliary classifying style-based generative adversarial network","author":"Kumaar","year":"2023","journal-title":"International Journal of Imaging Systems and Technology"},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0350","doi-asserted-by":"crossref","first-page":"500","DOI":"10.3348\/kjr.2022.0033","article-title":"Research highlight: Use of generative images created with artificial intelligence for 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and Mathematics"},{"key":"10.1016\/j.eswa.2026.131162_b0370","first-page":"1","article-title":"MMFGAN: A novel multimodal brain medical image fusion based on improved generative adversarial network","author":"Guo","year":"2022","journal-title":"Multimedia Tools and Applications"},{"key":"10.1016\/j.eswa.2026.131162_b0375","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2023.3304388","article-title":"Brain status transferring generative adversarial network for decoding individualized atrophy in Alzheimer\u2019s disease","author":"Gao","year":"2023","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2026.131162_b0380","series-title":"Proceedings of the International Conference on Artificial Intelligence in Information and Communication","article-title":"Anomaly detection for Alzheimer\u2019s disease in brain MRIs via unsupervised generative adversarial learning","author":"Cabreza","year":"2022"},{"key":"10.1016\/j.eswa.2026.131162_b0385","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.105387","article-title":"DualMMP-GAN: Dual-scale multi-modality perceptual generative adversarial network for medical image segmentation","volume":"144","author":"Zhu","year":"2022","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0390","doi-asserted-by":"crossref","DOI":"10.32604\/iasc.2023.032391","article-title":"Multi-level deep generative adversarial networks for brain tumor classification on magnetic resonance images","volume":"36","author":"Asiri","year":"2023","journal-title":"Intelligent Automation & Soft Computing"},{"key":"10.1016\/j.eswa.2026.131162_b0395","series-title":"AIP Conference Proceedings","doi-asserted-by":"crossref","DOI":"10.1063\/5.0174209","article-title":"Brain tumour image generation based on the deep convolutional generative adversarial network","author":"Su","year":"2023"},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0400","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.222878","article-title":"Accelerated cardiac MRI cine with use of resolution enhancement generative adversarial inline neural network","volume":"307","author":"Yoon","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.eswa.2026.131162_b0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2022.106513","article-title":"SwinGAN: A dual-domain Swin Transformer-based generative adversarial network for MRI reconstruction","volume":"153","author":"Zhao","year":"2023","journal-title":"Computers in Biology and Medicine"},{"issue":"2","key":"10.1016\/j.eswa.2026.131162_b0410","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s12149-022-01820-x","article-title":"A review of harmonization strategies for quantitative PET","volume":"37","author":"Akamatsu","year":"2023","journal-title":"Annals of Nuclear Medicine"},{"key":"10.1016\/j.eswa.2026.131162_b0415","article-title":"Assessing privacy leakage in synthetic 3-D PET imaging using transversal GAN","volume":"107910","author":"Bergen","year":"2023","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"10.1016\/j.eswa.2026.131162_b0420","unstructured":"Kollovieh, M., Eslami, A., Wachinger, C., & Navab, N. (2022). U-PET: MRI-based dementia detection with joint generation of synthetic FDG-PET images. arXiv preprint arXiv:2206.08078."},{"key":"10.1016\/j.eswa.2026.131162_b0425","unstructured":"Xue, Y., Zhang, H., Chen, Y., Wang, S., & Li, X. (2023). PET synthesis via self-supervised adaptive residual estimation generative adversarial network. arXiv preprint arXiv:2310.15550."},{"key":"10.1016\/j.eswa.2026.131162_b0430","doi-asserted-by":"crossref","unstructured":"Fedrigo, R., Mehranian, A., Reader, A. J., & Bertoldo, A. (2023). Observer study-based evaluation of TGAN architecture used to generate oncological PET images. arXiv preprint arXiv:2311.16047.","DOI":"10.1117\/12.2652661"},{"issue":"13","key":"10.1016\/j.eswa.2026.131162_b0435","doi-asserted-by":"crossref","first-page":"3267","DOI":"10.3390\/cancers15133267","article-title":"Recent advances in deep learning and medical imaging for head and neck cancer treatment: MRI, CT, and PET scans","volume":"15","author":"Illimoottil","year":"2023","journal-title":"Cancers"},{"key":"10.1016\/j.eswa.2026.131162_b0440","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.1053783","article-title":"A zero-dose synthetic baseline for personalized analysis of [18F]FDG-PET: Application in Alzheimer\u2019s disease","volume":"16","author":"Hinge","year":"2022","journal-title":"Frontiers in Neuroscience"},{"issue":"7","key":"10.1016\/j.eswa.2026.131162_b0445","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.3390\/medicina59071281","article-title":"Generation of conventional 18F-FDG PET images from 18F-florbetaben PET images using generative adversarial network","volume":"59","author":"Choi","year":"2023","journal-title":"Medicina"},{"issue":"3","key":"10.1016\/j.eswa.2026.131162_b0450","doi-asserted-by":"crossref","first-page":"2845","DOI":"10.1007\/s00521-022-07750-z","article-title":"Diagnosis of Alzheimer\u2019s, Parkinson\u2019s disease and frontotemporal dementia using a generative adversarial deep convolutional neural network","volume":"35","author":"Noella","year":"2023","journal-title":"Neural Computing and Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.131162_b0455","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1007\/s12350-022-03010-8","article-title":"Automated nonlinear registration of coronary PET to CT angiography using pseudo-CT generated from PET with generative adversarial networks","volume":"30","author":"Singh","year":"2023","journal-title":"Journal of Nuclear Cardiology"},{"key":"10.1016\/j.eswa.2026.131162_b0460","unstructured":"Sousa, H. S. (2022). A patch-wise generative adversarial network for PET-MR image generation with feature attribution for detection of focal cortical dysplasia (Master\u2019s thesis)."},{"key":"10.1016\/j.eswa.2026.131162_b0465","series-title":"Machine Learning in Cardiovascular Medicine","first-page":"95","article-title":"Generative adversarial network for cardiovascular imaging","author":"Rezaei","year":"2023"},{"key":"10.1016\/j.eswa.2026.131162_b0470","series-title":"Proceedings of MICCAI","article-title":"Classification-aided high-quality PET image synthesis via bidirectional contrastive GAN with shared information maximization","author":"Fei","year":"2022"},{"key":"10.1016\/j.eswa.2026.131162_b0475","series-title":"Proceedings of MICCAI","article-title":"3D CVT-GAN: A 3D convolutional vision transformer GAN for PET reconstruction","author":"Zeng","year":"2022"},{"key":"10.1016\/j.eswa.2026.131162_b0480","series-title":"Proceedings of the International Conference on Machine Vision and Applications","article-title":"Structure-enhanced translation from PET to CT modality with paired GANs","author":"Ahmed","year":"2023"},{"key":"10.1016\/j.eswa.2026.131162_b0485","doi-asserted-by":"crossref","DOI":"10.1016\/j.jdent.2022.104211","article-title":"A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level","volume":"123","author":"Kearney","year":"2022","journal-title":"Journal of Dentistry"},{"key":"10.1016\/j.eswa.2026.131162_b0490","doi-asserted-by":"crossref","DOI":"10.1016\/j.health.2023.100251","article-title":"A hybrid generative adversarial network with quantum U-Net for 3D spine X-ray image registration","volume":"4","author":"Gadu","year":"2023","journal-title":"Healthcare Analytics"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0495","doi-asserted-by":"crossref","first-page":"15","DOI":"10.31185\/wjcm.Vol1.Iss1.24","article-title":"COVID-19 patterns identification using generative adversarial networks","volume":"1","author":"Kh-Madhloom","year":"2022","journal-title":"Wasit Journal of Computer and Mathematics Science"},{"key":"10.1016\/j.eswa.2026.131162_b0500","series-title":"Proceedings of AISI","article-title":"Detection of COVID-19 associated pneumonia using generative adversarial networks and fine-tuned transfer learning","author":"Khalifa","year":"2022"},{"key":"10.1016\/j.eswa.2026.131162_b0505","unstructured":"Lee, H., Kim, J., Park, J., & Kang, K. (2023). Unified chest X-ray and radiology report generation model with multi-view chest X-rays. arXiv preprint arXiv:2302.12172."},{"issue":"10","key":"10.1016\/j.eswa.2026.131162_b0510","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.36100","article-title":"Generative artificial intelligence for chest radiograph interpretation in the emergency department","volume":"6","author":"Huang","year":"2023","journal-title":"JAMA Network Open"},{"key":"10.1016\/j.eswa.2026.131162_b0515","first-page":"1","article-title":"Comparative analysis of generative adversarial network models for image augmentation using COVID-19 X-ray and CT images","author":"Ubale Kiru","year":"2022","journal-title":"Journal of Intelligent & Fuzzy Systems"},{"key":"10.1016\/j.eswa.2026.131162_b0520","series-title":"ITM Web of Conferences","article-title":"Lung segmentation in chest X-ray images using generative adversarial networks","author":"El Mansouri","year":"2022"},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0525","article-title":"Screening of adolescent idiopathic scoliosis using GAN inversion method in chest radiographs","volume":"18","author":"Lee","year":"2023","journal-title":"PLoS One1"},{"issue":"12","key":"10.1016\/j.eswa.2026.131162_b0530","doi-asserted-by":"crossref","first-page":"1642","DOI":"10.3390\/jpm13121642","article-title":"Synthetic 3D spinal vertebrae reconstruction from biplanar X-rays using generative adversarial networks","volume":"13","author":"Saravi","year":"2023","journal-title":"Journal of Personalized Medicine"},{"key":"10.1016\/j.eswa.2026.131162_b0535","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107262","article-title":"Rapid diagnosis of COVID-19 infections using progressively growing GAN and CNN optimisation","volume":"229","author":"Gulakala","year":"2023","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"6","key":"10.1016\/j.eswa.2026.131162_b0540","doi-asserted-by":"crossref","DOI":"10.2196\/37365","article-title":"Combating COVID-19 using generative adversarial networks and artificial intelligence for medical images: A scoping review","volume":"10","author":"Ali","year":"2022","journal-title":"JMIR Medical Informatics"},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0545","doi-asserted-by":"crossref","first-page":"5444","DOI":"10.11591\/ijece.v12i5.pp5444-5454","article-title":"Realistic image synthesis of COVID-19 chest X-rays using boundary equilibrium GAN","volume":"12","author":"Iklima","year":"2022","journal-title":"International Journal of Electrical and Computer Engineering"},{"issue":"22","key":"10.1016\/j.eswa.2026.131162_b0550","doi-asserted-by":"crossref","first-page":"31201","DOI":"10.1007\/s11042-022-12640-6","article-title":"DGCNN: Deep convolutional generative adversarial network-based CNN for COVID-19 diagnosis","volume":"81","author":"Laddha","year":"2022","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0555","doi-asserted-by":"crossref","first-page":"18573","DOI":"10.1038\/s41598-022-23081-4","article-title":"DeepFake knee osteoarthritis X-rays generated by GANs deceive medical experts","volume":"12","author":"Prezja","year":"2022","journal-title":"Scientific Reports"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0560","doi-asserted-by":"crossref","first-page":"19186","DOI":"10.1038\/s41598-022-23692-x","article-title":"GAN-based data augmentation for CNN-based COVID-19 detection","volume":"12","author":"Gulakala","year":"2022","journal-title":"Scientific Reports"},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0565","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1007\/s42979-023-02002-w","article-title":"Automated COVID-19 diagnosis using synthetic chest X-ray images and deep feature fusion","volume":"4","author":"Mahanta","year":"2023","journal-title":"SN Computer Science"},{"key":"10.1016\/j.eswa.2026.131162_b0570","doi-asserted-by":"crossref","DOI":"10.1016\/j.sciaf.2023.e01679","article-title":"Automatic classification of ultrasound thyroid images using vision transformers and generative adversarial networks","volume":"20","author":"Jerbi","year":"2023","journal-title":"Scientific African"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0575","first-page":"37","article-title":"Discrimination-performance evaluation of arteries and veins in ultrasound images by data augmentation using GAN","volume":"3","author":"Inoue","year":"2022","journal-title":"Chitose Institute of Science and Technology Bulletin"},{"key":"10.1016\/j.eswa.2026.131162_b0580","unstructured":"Bautista, T., Jimenez, J., Rueda, A., & Montoya, O. (2022). Empirical study of quality image assessment for synthesis of fetal head ultrasound imaging with DCGANs. arXiv preprint arXiv:2206.01731."},{"key":"10.1016\/j.eswa.2026.131162_b0585","series-title":"International Workshop on Simulation and Synthesis in Medical Imaging","article-title":"How good are synthetic medical images? An empirical study with lung ultrasound","author":"Yu","year":"2023"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0590","first-page":"12","article-title":"Ultrasound image synthesis using deep convolutional generative adversarial networks for breast cancer identification","volume":"34","author":"Haq","year":"2023","journal-title":"IPTEK: The Journal for Technology and Science"},{"key":"10.1016\/j.eswa.2026.131162_b0595","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JTEHM.2022.3199987","article-title":"Automatic hemorrhage detection from color Doppler ultrasound using GAN-based anomaly detection","volume":"10","author":"Mitra","year":"2022","journal-title":"IEEE Journal of Translational Engineering in Health and Medicine"},{"issue":"2","key":"10.1016\/j.eswa.2026.131162_b0600","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3390\/bioengineering10020184","article-title":"Improving the segmentation accuracy of ovarian tumor ultrasound images using image inpainting","volume":"10","author":"Chen","year":"2023","journal-title":"Bioengineering"},{"issue":"2","key":"10.1016\/j.eswa.2026.131162_b0605","doi-asserted-by":"crossref","first-page":"253","DOI":"10.3390\/diagnostics12020253","article-title":"AUE-Net: Automated generation of ultrasound elastography using generative adversarial network","volume":"12","author":"Zhang","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.eswa.2026.131162_b0610","series-title":"Proceedings of the 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","article-title":"Exploiting class activation mappings as prior to generate fetal brain ultrasound images with GANs","author":"Lasala","year":"2023"},{"key":"10.1016\/j.eswa.2026.131162_b0615","article-title":"Classification of breast masses using ultrasound images by approaching GAN, transfer learning, and deep learning techniques","author":"Chaudhury","year":"2023","journal-title":"Journal of Artificial Intelligence and Technology."},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0620","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3390\/medicina60010014","article-title":"Clinical utility of breast ultrasound images synthesized by a generative adversarial network","volume":"60","author":"Zama","year":"2023","journal-title":"Medicina"},{"key":"10.1016\/j.eswa.2026.131162_b0625","doi-asserted-by":"crossref","first-page":"e873","DOI":"10.7717\/peerj-cs.873","article-title":"Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss","volume":"8","author":"Zhang","year":"2022","journal-title":"PeerJ Computer Science"},{"key":"10.1016\/j.eswa.2026.131162_b0630","first-page":"1","article-title":"Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification","author":"Zhao","year":"2022","journal-title":"Applied Intelligence"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0635","doi-asserted-by":"crossref","first-page":"9533","DOI":"10.1038\/s41598-022-13658-4","article-title":"A new generative adversarial network for medical image super-resolution","volume":"12","author":"Ahmad","year":"2022","journal-title":"Scientific Reports"},{"issue":"20","key":"10.1016\/j.eswa.2026.131162_b0640","doi-asserted-by":"crossref","first-page":"8614","DOI":"10.3390\/s23208614","article-title":"2S-BUSGAN: A novel generative adversarial network for realistic breast ultrasound images with corresponding tumor contours based on small datasets","volume":"23","author":"Luo","year":"2023","journal-title":"Sensors"},{"issue":"4","key":"10.1016\/j.eswa.2026.131162_b0645","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.1109\/JBHI.2022.3153559","article-title":"GAN-guided deformable attention network for identifying thyroid nodules in ultrasound 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networks","author":"Xiang","year":"2023"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0665","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.diii.2022.09.005","article-title":"Generative adversarial network-based data augmentation of rare liver cancers: The SFR 2021 artificial intelligence data challenge","volume":"104","author":"Mul\u00e9","year":"2023","journal-title":"Diagnostic and Interventional Imaging"},{"key":"10.1016\/j.eswa.2026.131162_b0670","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106753","article-title":"Data augmentation-guided breast tumor segmentation based on generative adversarial neural networks","volume":"125","author":"Kannappan","year":"2023","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.131162_b0675","doi-asserted-by":"crossref","DOI":"10.1088\/1361-6560\/acdbb4","article-title":"A cGAN-based tumor segmentation method for breast ultrasound images","author":"You","year":"2023","journal-title":"Physics in Medicine & Biology"},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0680","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1007\/s40123-023-00775-0","article-title":"Deep learning applications to classification and detection of age-related macular degeneration on optical coherence tomography imaging: A review","volume":"12","author":"Koseoglu","year":"2023","journal-title":"Ophthalmology and Therapy"},{"key":"10.1016\/j.eswa.2026.131162_b0685","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2022.103957","article-title":"Image enhancement of wide-field retinal OCT angiography by super-resolution angiogram reconstruction generative adversarial network","volume":"78","author":"Yuan","year":"2022","journal-title":"Biomedical Signal Processing and Control"},{"key":"10.1016\/j.eswa.2026.131162_b0690","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2023.106595","article-title":"Segmentation-guided domain adaptation and data harmonization of multi-device retinal OCT using cycle-consistent generative adversarial networks","volume":"159","author":"Chen","year":"2023","journal-title":"Computers in Biology and Medicine"},{"issue":"4","key":"10.1016\/j.eswa.2026.131162_b0695","doi-asserted-by":"crossref","first-page":"2195","DOI":"10.3390\/app13042195","article-title":"A glaucoma detection system based on generative adversarial network and incremental learning","volume":"13","author":"Chang","year":"2023","journal-title":"Applied Sciences"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0700","doi-asserted-by":"crossref","first-page":"50","DOI":"10.4103\/pajo.pajo_62_23","article-title":"Automated ophthalmic imaging analysis in the era of generative pre-trained transformer-4","volume":"5","author":"Waisberg","year":"2023","journal-title":"The Pan-American Journal of Ophthalmology"},{"key":"10.1016\/j.eswa.2026.131162_b0705","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2022.914964","article-title":"Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network","volume":"10","author":"Xu","year":"2022","journal-title":"Frontiers in Bioengineering and Biotechnology"},{"key":"10.1016\/j.eswa.2026.131162_b0710","article-title":"Prediction of corneal astigmatism based on corneal tomography after femtosecond laser arcuate keratotomy using a pix2pix conditional generative adversarial network","volume":"10","author":"Zhang","year":"2022","journal-title":"Frontiers in Public Health"},{"key":"10.1016\/j.eswa.2026.131162_b0715","series-title":"International Workshop on Simulation and Synthesis in Medical Imaging","article-title":"Contrastive learning for generating optical coherence tomography images of the retina","author":"Kaplan","year":"2022"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0720","doi-asserted-by":"crossref","first-page":"5639","DOI":"10.1038\/s41598-023-32398-7","article-title":"Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network","volume":"13","author":"Moon","year":"2023","journal-title":"Scientific Reports"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0725","doi-asserted-by":"crossref","first-page":"15325","DOI":"10.1038\/s41598-023-42062-9","article-title":"Synthetic OCT-A blood vessel maps using fundus images and generative adversarial networks","volume":"13","author":"Coronado","year":"2023","journal-title":"Scientific Reports"},{"issue":"1","key":"10.1016\/j.eswa.2026.131162_b0730","doi-asserted-by":"crossref","first-page":"19960","DOI":"10.1038\/s41598-023-46253-2","article-title":"Predicting glaucoma progression using deep learning framework guided by generative algorithm","volume":"13","author":"Hussain","year":"2023","journal-title":"Scientific Reports"},{"key":"10.1016\/j.eswa.2026.131162_b0735","unstructured":"Schmetterer, L., Garh\u00f6fer, G., Werkmeister, R. M., & Leitgeb, R. A. (2023). Optical coherence tomography choroidal enhancement using generative deep learning."},{"key":"10.1016\/j.eswa.2026.131162_b0740","doi-asserted-by":"crossref","DOI":"10.1109\/JBHI.2023.3252665","article-title":"LAGAN: Lesion-aware generative adversarial networks for edema area segmentation in SD-OCT images","author":"Tao","year":"2023","journal-title":"IEEE Journal of Biomedical and Health Informatics."},{"issue":"11","key":"10.1016\/j.eswa.2026.131162_b0745","doi-asserted-by":"crossref","first-page":"7357","DOI":"10.1002\/mp.15988","article-title":"Automatic generation of retinal optical coherence tomography images based on generative adversarial networks","volume":"49","author":"Zhao","year":"2022","journal-title":"Medical Physics"},{"issue":"6","key":"10.1016\/j.eswa.2026.131162_b0750","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1515\/bmt-2022-0286","article-title":"Atherosclerosis plaque tissue classification using self-attention-based conditional variational autoencoder-generative adversarial network using OCT plaque images","volume":"68","author":"Jagadeesan","year":"2023","journal-title":"Biomedical Engineering \/ Biomedizinische Technik"},{"key":"10.1016\/j.eswa.2026.131162_b0755","series-title":"Glaucoma progression detection and Humphrey visual field prediction using discriminative and generative vision transformers","first-page":"62","author":"Tian","year":"2023"},{"issue":"7","key":"10.1016\/j.eswa.2026.131162_b0760","doi-asserted-by":"crossref","first-page":"863","DOI":"10.2174\/1573405614666191115102318","article-title":"Role of generative adversarial networks in mammogram analysis: A review","volume":"16","author":"Gopal","year":"2020","journal-title":"Current Medical Imaging"},{"key":"10.1016\/j.eswa.2026.131162_b0765","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.106884","article-title":"Early detection and classification of abnormality in prior mammograms using image-to-image translation and YOLO techniques","volume":"221","author":"Baccouche","year":"2022","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"9","key":"10.1016\/j.eswa.2026.131162_b0770","doi-asserted-by":"crossref","first-page":"859","DOI":"10.3390\/biology10090859","article-title":"An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer learning","volume":"10","author":"Mahmood","year":"2021","journal-title":"Biology"},{"key":"10.1016\/j.eswa.2026.131162_b0775","doi-asserted-by":"crossref","unstructured":"Kidder, B. 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Technical outlier detection via convolutional variational autoencoder for the ADMANI breast mammogram dataset. arXiv preprint arXiv:2305.12068."},{"issue":"5","key":"10.1016\/j.eswa.2026.131162_b0785","doi-asserted-by":"crossref","DOI":"10.1117\/1.JMI.10.5.054503","article-title":"Impact of GAN artifacts for simulating mammograms on identifying mammographically occult cancer","volume":"10","author":"Lee","year":"2023","journal-title":"Journal of Medical Imaging"},{"issue":"23","key":"10.1016\/j.eswa.2026.131162_b0790","doi-asserted-by":"crossref","first-page":"12206","DOI":"10.3390\/app122312206","article-title":"Two-view mammogram synthesis from single-view data using generative adversarial networks","volume":"12","author":"Yamazaki","year":"2022","journal-title":"Applied Sciences"},{"issue":"7","key":"10.1016\/j.eswa.2026.131162_b0795","doi-asserted-by":"crossref","first-page":"4272","DOI":"10.3390\/app13074272","article-title":"Applying deep learning methods for mammography analysis 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undetected findings in mammograms","volume":"4","author":"Anyfantis","year":"2023","journal-title":"Signals"},{"key":"10.1016\/j.eswa.2026.131162_b0860","series-title":"Machine Intelligence and Data Science Applications: Proceedings of MIDAS 2021","first-page":"761","article-title":"A deep convolutional generative adversarial network-based model to analyze histopathological breast cancer images","author":"Tani","year":"2022"},{"key":"10.1016\/j.eswa.2026.131162_b0865","series-title":"Advances in Neural Information Processing Systems","first-page":"30","article-title":"GANs trained by a two-time-scale update rule converge to a local Nash equilibrium","author":"Heusel","year":"2017"},{"key":"10.1016\/j.eswa.2026.131162_b0870","unstructured":"Bi\u0144kowski, M., Sutherland, D. 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