ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2020
CycleGAN-based deep learning technique for artifact reduction in fundus photography.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning.Translational vision science & technology · 2025Article
- Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery.Scientific reports · 2025Article
- Versatile cataract fundus image restoration model utilizing unpaired cataract and high-quality images.Scientific reports · 2025Article
- Based on TransRes-Pix2Pix network to generate the OBL image during SMILE surgery.Frontiers in cell and developmental biology · 2025Article
- Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges.The British journal of ophthalmology · 2024Review
- Physics-informed deep generative learning for quantitative assessment of the retina.Nature communications · 2024Article
- Synthesizing High b-Value Diffusion-Weighted Imaging of Gastric Cancer Using an Improved Vision Transformer CycleGAN.Bioengineering (Basel, Switzerland) · 2024Article
- Diagnostic performance of deep-learning-based virtual chromoendoscopy in gastric neoplasms.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence.Annals of biomedical engineering · 2023Review
- Addressing inter-device variations in optical coherence tomography angiography: will image-to-image translation systems help?International journal of retina and vitreous · 2023Article
- Retinal Scans and Data Sharing: The Privacy and Scientific Development Equilibrium.Mayo Clinic proceedings. Digital health · 2023Review
- A Global Review of Publicly Available Datasets Containing Fundus Images: Characteristics, Barriers to Access, Usability, and Generalizability.Journal of clinical medicine · 2023Article
- Asteroid Hyalosis: An Update on Prevalence, Risk Factors, Emerging Clinical Impact and Management Strategies.Clinical ophthalmology (Auckland, N.Z.) · 2023Review
- Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.JAMA ophthalmology · 2022Article
- Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.Progress in retinal and eye research · 2022Review
- Assessment of image quality on color fundus retinal images using the automatic retinal image analysis.Scientific reports · 2022Article
- Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey.Eye and vision (London, England) · 2022Review
- Connected-UNets: a deep learning architecture for breast mass segmentation.NPJ breast cancer · 2021Article
- RF-GANs: A Method to Synthesize Retinal Fundus Images Based on Generative Adversarial Network.Computational intelligence and neuroscience · 2021Article
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Authors and funding
3 authors.
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Abstract
purposeA low quality of fundus photograph with artifacts may lead to false diagnosis. Recently, a cycle-consistent generative adversarial network (CycleGAN) has been introduced as a tool to generate images without matching paired images. Therefore, herein, we present a deep learning technique that removes the artifacts automatically in a fundus photograph using a CycleGAN model.
methodsThis study included a total of 2206 anonymized retinal images including 1146 with artifacts and 1060 without artifacts. In this experiment, we applied the CycleGAN model to color fundus photographs with a pixel resolution of 256 × 256 × 3. To apply the CycleGAN to an independent dataset, we randomly divided the data into training (90%) and test (10%) datasets. Additionally, we adopted the automated quality evaluation (AQE) to assess the retinal image quality.
resultsFrom the results, we observed that the artifacts such as overall haze, edge haze, lashes, arcs, and uneven illumination were successfully reduced by the CycleGAN in the generated images, and the main information of the retina was essentially retained. Further, we observed that most of the generated images exhibited improved AQE grade values when compared with the original images with artifacts.
conclusionThus, we could conclude that the CycleGAN technique can effectively reduce the artifacts and improve the quality of fundus photographs, and it may be beneficial for clinicians in analyzing the low-quality fundus photographs. Future studies should improve the quality and resolution of the generated image to provide a more detailed fundus photography.
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