ArticleBiomedical optics express2023
High-fidelity diabetic retina fundus image synthesis from freestyle lesion maps.
Article in Biomedical optics express, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Augmenting a prognostic deep learning system for referable diabetic retinopathy and maculopathy with synthetic retinal images.Communications medicine · 2025Article
- Development and validation of an AI algorithm to generate realistic and meaningful counterfactuals for retinal imaging based on diffusion models.PLOS digital health · 2025Article
- Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop.Nature reviews. Cardiology · 2025Review
- Introduction to Visual and Physiological Optics feature issue ofBiomedical optics express · 2023Article
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Retina fundus imaging for diagnosing diabetic retinopathy (DR) is an efficient and patient-friendly modality, where many high-resolution images can be easily obtained for accurate diagnosis. With the advancements of deep learning, data-driven models may facilitate the process of high-throughput diagnosis especially in areas with less availability of certified human experts. Many datasets of DR already exist for training learning-based models. However, most are often unbalanced, do not have a large enough sample count, or both. This paper proposes a two-stage pipeline for generating photo-realistic retinal fundus images based on either artificially generated or free-hand drawn semantic lesion maps. The first stage uses a conditional StyleGAN to generate synthetic lesion maps based on a DR severity grade. The second stage then uses GauGAN to convert the synthetic lesion maps into high resolution fundus images. We evaluate the photo-realism of generated images using the Fréchet inception distance (FID), and show the efficacy of our pipeline through downstream tasks, such as; dataset augmentation for automatic DR grading and lesion segmentation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.