ArticleProceedings. IEEE International Symposium on Biomedical Imaging2023
OPTIMAL TRANSPORT GUIDED UNSUPERVISED LEARNING FOR ENHANCING LOW-QUALITY RETINAL IMAGES.
Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- AI framework for multidisease detection via retinal imaging.Nature medicine · 2026Article
- Unified Framework for Enhancement of Low-Quality Fundus Images.Journal of imaging informatics in medicine · 2026Article
- Comparing Interactive Machine Learning and Unassisted Segmentation of Geographic Atrophy From Fundus Autofluorescence.Translational vision science & technology · 2026Article
- TPOT: TOPOLOGY PRESERVING OPTIMAL TRANSPORT IN RETINAL FUNDUS IMAGE ENHANCEMENT.Proceedings. IEEE International Symposium on Biomedical Imaging · 2025Article
- Color Fundus Photography and Deep Learning Applications in Alzheimer Disease.Mayo Clinic proceedings. Digital health · 2024Article
- RBAD: A Dataset and Benchmark for Retinal Vessels Branching Angle Detection.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- Robust PCA withJournal of imaging · 2024Article
- nnMobileNet: Rethinking CNN for Retinopathy Research.Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops · 2024Article
- RECONSTRUCTING RETINAL VISUAL IMAGES FROM 3T FMRI DATA ENHANCED BY UNSUPERVISED LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- ESDiff: a joint model for low-quality retinal image enhancement and vessel segmentation using a diffusion model.Biomedical optics express · 2023Article
- CUNSB-RFIE: Context-aware Unpaired Neural Schrödinger Bridge in Retinal Fundus Image Enhancement.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
- Context-Aware Optimal Transport Learning for Retinal Fundus Image Enhancement.IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer VisionArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Real-world non-mydriatic retinal fundus photography is prone to artifacts, imperfections, and low-quality when certain ocular or systemic co-morbidities exist. Artifacts may result in inaccuracy or ambiguity in clinical diagnoses. In this paper, we proposed a simple but effective end-to-end framework for enhancing poor-quality retinal fundus images. Leveraging the optimal transport theory, we proposed an unpaired image-to-image translation scheme for transporting low-quality images to their high-quality counterparts. We theoretically proved that a Generative Adversarial Networks (GAN) model with a generator and discriminator is sufficient for this task. Furthermore, to mitigate the inconsistency of information between the low-quality images and their enhancements, an information consistency mechanism was proposed to maximally maintain structural consistency (optical discs, blood vessels, lesions) between the source and enhanced domains. Extensive experiments were conducted on the EyeQ dataset to demonstrate the superiority of our proposed method perceptually and quantitatively.
Indexed as
Identifiers
What OpenQuestion holds
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.