ArticleScientific reports2022
Synthesizing realistic high-resolution retina image by style-based generative adversarial network and its utilization.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Generative adversarial networks (GAN) for pre-dilation retinal photograph quality enhancement.Eye (London, England) · 2026Article
- Conditional Latent Diffusion for Controllable Palpebral Conjunctiva Image Generation Toward Hemoglobin Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- BrAIn: A comprehensive artificial intelligence-based morphology analysis system for brain organoids and neuroscience.Bioengineering & translational medicine · 2026Article
- AI-Based RGB Image Generation From RG Fundus Images Using Pix2Pix: Validation by Quantitative and Observer-Based Evaluations.Translational vision science & technology · 2026Article
- Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis.Progress in retinal and eye research · 2026Review
- Augmenting a prognostic deep learning system for referable diabetic retinopathy and maculopathy with synthetic retinal images.Communications medicine · 2025Article
- Clinical evaluation of medical image synthesis: a case study in wireless capsule endoscopy.Scientific reports · 2025Article
- AI deepfake: GPT-4o can produce near-authentic fundus images.Eye (London, England) · 2025Article
- Developing an artificial intelligence-based progressive growing GAN for high-quality facial profile generation and evaluation through turing test and aesthetic analysis.Scientific reports · 2025Article
- Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.BMC medical imaging · 2025Article
- Generative Artificial Intelligence Use in Healthcare: Opportunities for Clinical Excellence and Administrative Efficiency.Journal of medical systems · 2025Article
- Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop.Nature reviews. Cardiology · 2025Review
- Synthetic Data Generation via Generative Adversarial Networks in Healthcare: A Systematic Review of Image- and Signal-Based Studies.IEEE open journal of engineering in medicine and biology · 2025Article
- VSG-GAN: A high-fidelity image synthesis method with semantic manipulation in retinal fundus image.Biophysical journal · 2024Article
- A Clinician's Guide to Sharing Data for AI in Ophthalmology.Investigative ophthalmology & visual science · 2024Review
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- Improving nonalcoholic fatty liver disease classification performance with latent diffusion models.Scientific reports · 2023Article
- Deep learning and clustering approaches for dental implant size classification based on periapical radiographs.Scientific reports · 2023Article
- Evaluating clinical diversity and plausibility of synthetic capsule endoscopic images.Scientific reports · 2023Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Realistic image synthesis based on deep learning is an invaluable technique for developing high-performance computer aided diagnosis systems while protecting patient privacy. However, training a generative adversarial network (GAN) for image synthesis remains challenging because of the large amounts of data required for training various kinds of image features. This study aims to synthesize retinal images indistinguishable from real images and evaluate the efficacy of the synthesized images having a specific disease for augmenting class imbalanced datasets. The synthesized images were validated via image Turing tests, qualitative analysis by retinal specialists, and quantitative analyses on amounts and signal-to-noise ratios of vessels. The efficacy of synthesized images was verified by deep learning-based classification performance. Turing test shows that accuracy, sensitivity, and specificity of 54.0 ± 12.3%, 71.1 ± 18.8%, and 36.9 ± 25.5%, respectively. Here, sensitivity represents correctness to find real images among real datasets. Vessel amounts and average SNR comparisons show 0.43% and 1.5% difference between real and synthesized images. The classification performance after augmenting synthesized images outperforms every ratio of imbalanced real datasets. Our study shows the realistic retina images were successfully generated with insignificant differences between the real and synthesized images and shows great potential for practical applications.
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