ArticleTranslational vision science & technology2021
Pix2pix Conditional Generative Adversarial Networks for Scheimpflug Camera Color-Coded Corneal Tomography Image Generation.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Comparison of different corneal imaging modalities using artificial intelligence for diagnosis of keratoconus: a systematic review and meta-analysis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Pooled it
- Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review.Frontiers in ophthalmology · 2024Pooled it
- Bridging the resources gap: deep learning for fluorescein angiography and optical coherence tomography macular thickness map image translation.BMC ophthalmology · 2022Trial
- Residual Conditional Variational Autoencoder for Multi-Center PET/CT Radiomic Feature Harmonization with Integrated Modeling of Batch Effects and Clinical Covariates.Journal of imaging informatics in medicine · 2026Article
- DiFFA: A Diffusion-Based Model for Synthetic Fundus Fluorescein Angiography to Enhance Detection of Diabetic Microvascular Complication.Translational vision science & technology · 2026Observational
- Artificial intelligence for diagnosis of keratoconus using Scheimpflug based corneal tomography.International journal of ophthalmology · 2026Article
- 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
- 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
- Based on TransRes-Pix2Pix network to generate the OBL image during SMILE surgery.Frontiers in cell and developmental biology · 2025Article
- Refining CycleGAN with attention mechanisms and age-Aware training for realistic Deepfakes.Heliyon · 2024Article
- Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening.NPJ digital medicine · 2024Article
- Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.Scientific reports · 2023Article
- Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.JAMA ophthalmology · 2022Article
- A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning.Ophthalmology science · 2022Article
- Towards effective data sharing in ophthalmology: data standardization and data privacy.Current opinion in ophthalmology · 2022Review
- Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey.Eye and vision (London, England) · 2022Review
- Prediction of corneal astigmatism based on corneal tomography after femtosecond laser arcuate keratotomy using a pix2pix conditional generative adversarial network.Frontiers in public health · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Purpose: To assess the ability of pix2pix conditional generative adversarial network (pix2pix cGAN) to create plausible synthesized Scheimpflug camera color-coded corneal tomography images based upon a modest-sized original dataset to be used for image augmentation during training a deep convolutional neural network (DCNN) for classification of keratoconus and normal corneal images. Methods: Original images of 1778 eyes of 923 nonconsecutive patients with or without keratoconus were retrospectively analyzed. Images were labeled and preprocessed for use in training the proposed pix2pix cGAN. The best quality synthesized images were selected based on the Fréchet inception distance score, and their quality was studied by calculating the mean square error, structural similarity index, and the peak signal-to-noise ratio. We used original, traditionally augmented original and synthesized images to train a DCNN for image classification and compared classification performance metrics. Results: The pix2pix cGAN synthesized images showed plausible subjectively and objectively assessed quality. Training the DCNN with a combination of real and synthesized images allowed better classification performance compared with training using original images only or with traditional augmentation. Conclusions: Using the pix2pix cGAN to synthesize corneal tomography images can overcome issues related to small datasets and class imbalance when training computer-aided diagnostic models. Translational Relevance: Pix2pix cGAN can provide an unlimited supply of plausible synthetic Scheimpflug camera color-coded corneal tomography images at levels useful for experimental and clinical applications.
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