Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI

Fan Xiao; Jiayi Liu; Lihong Jiang; Wengan He; Wenhui Xu; Yongsheng Luo; Yuanzhu Liang; Yu Li; Yuxi Chen; Zhuohang Huang

REVIEW 2 major objections 4 minor 300 references

Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI

T0 review · 2 major / 4 minor · reviewed 2026-07-31 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This review argues that progress in color fundus photography AI will now come from jointly improving datasets, preprocessing, and multimodal modeling, not from scaling model size alone.

desk verdict Useful, encyclopedic CFP dataset/preprocessing reference wrapped in an over-sold co-evolution thesis; deserves review with revisions. read the letter →

arxiv 2607.23972 v1 pith:2ZJNRE5N submitted 2026-07-27 cs.CV

Yu Li , Wengan He , Wenhui Xu , Lihong Jiang , Fan Xiao , Zhuohang Huang , Yuanzhu Liang , Jiayi Liu

show 2 more authors

Yuxi Chen Yongsheng Luo

This is my paper

classification cs.CV

keywords colorfundusphotographymedicalimageanalysisdatasetevolutionpreprocessingmultimodalAIfoundationmodelsophthalmologysurvey

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The review surveys roughly two decades of color fundus photography (CFP) analysis and argues that the field's progress has been driven by the joint evolution of three things: datasets, preprocessing, and model architectures. It claims that the current ceiling on diagnostic performance is set not by model size or image resolution alone, but by how well data curation, preprocessing, and multimodal context are optimized together. The practical consequence is that future gains should come from investing in data quality, standardized preprocessing, and fusion with electronic health records, rather than from stacking larger networks.

What carries the argument

The carrying mechanism is a tri-axial co-evolution framework: the paper reads the history of CFP AI as the interplay of three interacting axes — dataset evolution (scale, annotation granularity, modality), preprocessing paradigms (from handcrafted enhancement to neural and hardware-aware pipelines to cross-modal imputation), and modeling frameworks (from CNNs to vision transformers, state space models, and multimodal expert architectures). These three axes are presented as mutually constraining: each era's algorithmic choices are shaped by the data and preprocessing available, and each new modeling paradigm redefines what preprocessing must do.

What would settle it

A prospective benchmark that holds one axis fixed and varies the others — for example, training a fixed-architecture model on datasets of increasing curation quality while reporting diagnostic accuracy — would directly test whether accuracy gains track data/preprocessing improvements more than model scaling. If accuracy saturates regardless of data quality once model size is sufficient, the claim weakens.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a historical pattern: early CFP systems were limited by small single-disease datasets and handcrafted preprocessing; CNN-era systems benefited from large-scale curated datasets and learned preprocessing; and modern foundation and multimodal models push against a new barrier that is now primarily data- and preprocessing-bound. The review concludes that the upper bound of clinical diagnostic reliability is determined by collaborative optimization across the entire life cycle of data curation, structural quality assurance, and multimodal context synthesis.

Load-bearing premise

The premise that the three historical trends (data, preprocessing, and models) are causally linked as a co-evolution rather than parallel independent advances; if they moved independently, the review's conclusion that joint optimization determines the performance ceiling loses its foundation.

Editorial extensions

If this is right

If the review's framing is right, scaling model parameters or input resolution alone will yield diminishing returns for CFP diagnosis.
Investment in multi-center, multimodal, longitudinally linked datasets becomes a primary lever for improving diagnostic reliability and cross-domain generalization.
Preprocessing should be treated as a first-class engineering component — including hardware-aware token pruning and self-supervised imputation for missing EHR data — rather than a post-hoc auxiliary step.
Evaluations that do not standardize data curation and preprocessing protocols will be unable to compare algorithmic contributions fairly.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension: controlled studies that isolate one axis (e.g., fixing architecture and varying only dataset curation or preprocessing quality) would directly quantify the paper's claimed performance ceiling shift.
The tri-axial narrative could be sharpened by comparing it with alternative explanations such as compute scaling or benchmark competition; the review does not perform that comparison.
If the claim holds, clinical deployment roadmaps should budget for data governance and preprocessing infrastructure at least as heavily as for model R&D.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Colleague,

This is a 77-page survey of color fundus photography AI, and the part worth keeping is the catalog. The dataset tables list roughly 72 public datasets with sizes, tasks, annotation types, and honest per-dataset limitations; Table 7 inventories preprocessing methods; Table 2 walks the algorithm lineage. Spot-checks against the literature — DRIVE=40, STARE=400, MESSIDOR=1.2K, Eyecare-100K, RETFound — check out. That alone makes it a desktop reference for anyone choosing datasets or writing a background section.

The paper's organizing idea — data, preprocessing, and modeling co-evolve — is genuinely new as a framing device and the three-part structure works as a way to tell the story. But the abstract and §4.5/§5.1 overclaim: the review 'establishes' that the diagnostic ceiling is set by collaborative optimization across all three axes. That's an interpretive roadmap, not an established result. The paper never tests the causal coupling against alternatives like compute scaling, benchmark competition, or funding cycles, and the stage boundaries are imposed, not derived. If the axes moved independently, the conclusion loses its footing.

Soft spots in proportion: the performance tables mix metrics from heterogeneous evaluation protocols without caveats — a known problem in surveys, but worth a cautionary note. Some claims lean on 'publicly available documentation' without a citation. And the prose has mechanical artifacts, typos, and repetition that suggest it skipped a final copyedit. These are all fixable. I don't see fabricated data or self-consistency loops; the content is external and transcribed carefully enough to trust the overall inventory.

Who should read it: anyone needing a map of CFP datasets and preprocessing options, or a historical overview for a grant application. It deserves a serious referee: an editor should send it out, with the expectation that the authors soften the 'establishes' language, add a limitations paragraph acknowledging the co-evolution premise is a frame, and caveat the performance tables. After that it would be a standard reference.

Recommendation: engage with it. I'd take it for review, not desk reject.

Referee Report

2 major / 4 minor

Summary. This review surveys color fundus photography (CFP) AI through a tri-axial lens — dataset evolution, preprocessing paradigms, and modeling frameworks. Datasets are staged into pre-2014, 2015–2020, and 2021–present (§2); preprocessing into traditional, deep-learning-assisted, hybrid, and multimodal families (§3); and algorithms into knowledge-driven, CNN, unimodal global/foundation, and multimodal paradigms (§4). Extensive inventories are tabulated (Tables 1–10). The central conclusion, stated in the abstract, §4.5, and §5.1, is that the upper bound of clinical diagnostic performance is no longer set by pixel-resolution scaling or algorithmic parameter depth, but by collaborative optimization across data curation, structural quality assurance, and multimodal context synthesis. A five-point roadmap closes the paper (§5.2).

Significance. If accepted, the tri-axial synthesis provides a genuinely integrated reference: preprocessing is treated as a first-class object of study rather than an auxiliary footnote; the dataset matrices (Tables 1, 3–6) and preprocessing taxonomy (Table 7) are detailed and spot-check-consistent with primary sources; and each paradigm closes with an honest 'Methodological Boundaries' discussion, with explicit per-axis limitation sections (§2.2, §3.5). The paper is transparent — Table 9 discloses secondary-review-derived metrics, and the generative-AI writing declaration is included. Its roadmap makes falsifiable direction-setting claims (e.g., that data curation and multimodal standardization, not architecture scaling, will drive the next gains), which could usefully shape benchmarking and funding. The weakness is evidential calibration: the central claim is an interpretive synthesis presented with 'establishes/reveals' language.

major comments (2)

[Abstract; §1; §2.1; §3; §4.5; §5.1] Abstract/§5.1 assert the central claim as an established result: 'the upper bound of clinical diagnostic performance ... is no longer bounded strictly by pixel-resolution scaling or algorithmic parameter depth, but rather by the collaborative optimization across the entire life cycle' (§5.1). The co-evolution it presupposes is imposed, not tested: §1 and Figure 1 posit the tri-axial interplay as a frame; §2.1 assigns stage boundaries (before 2014 / 2015–2020 / 2021+) by fiat; §3 admits its taxonomy is chosen 'to sustain the overarching theme of tri-axial co-evolution.' No section tests coupling against alternatives (compute scaling, benchmark competition, funding cycles) or establishes that preprocessing causally drives downstream gains. The conclusion is a plausible roadmap, not a demonstrated finding. Recommend reframing the abstract and §4.5/§5.1 as synthesis/proposal, citing the coup
[Tables 2, 8–10; §4.3–4.4] Tables 2, 8–10 transcribe performance numbers from heterogeneous protocols: Table 8 mixes PSNR/SSIM/accuracy measured on DRIVE, DIARETDB1, and Kaggle DR under different conditions; Table 2 mixes AUC, QWK, C-index, and DSC across tasks and datasets; Table 9's note discloses that some metrics were extracted from a secondary review [143]. No audit of splits, metric definitions, or preprocessing confounds is provided. These tables feed the 'progress' narrative and the ceiling assertions in §4.3 ('final performance ceiling of pure vision') and §4.4 ('approaches its structural and performance limits'). Since those assertions are load-bearing for the multimodal-turn argument, the tables need a protocol-mismatch caveat or the quantitative-progress claims should be explicitly softened.

minor comments (4)

[§2.1 and Appendices A–D] In-text pointers to 'Appendix 1'–'Appendix 4' do not match the lettered appendix headings (A–D for dataset stages), and appendix tables are numbered 3–10 without a letter prefix, which is ambiguous against the main-text numbering (Tables 1–2).
[Table 7] Item numbering skips 22 (the sequence goes 21, 23).
[Headings and captions] Typographical issues: 'Acknowledegments' (heading), 'datase' (Figure 6 caption), 'Multi-modal Preprocess' (§3.4 heading appears truncated), and 'Algorithm' in the Figure 1 caption should be pluralized.
[Abstract] The abstract describes recent registries as having 'uncurated, heterogeneous longitudinal profiles,' which sits oddly with §2.1.3's account of 'highly formalized, automated processing pipelines' in the same cohorts; consider aligning the wording.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation chain; the paper is a narrative survey whose central claim is interpretive, not a fitted result.

full rationale

This is a literature review, not a derivation. It contains no equations, no fitted parameters, and no author-owned uniqueness theorem that is invoked to force a conclusion. The abstract and §4.5/§5.1 claim that the upper bound of clinical diagnostic performance is determined by collaborative optimization across data curation, preprocessing, and multimodal context. That claim is a synthesis of the reviewed external literature, not a quantity computed from the review's own inputs. The paper explicitly acknowledges its own evidentiary limits, e.g., §3.5.1: 'existing evaluation frameworks rely on unstandardized, heterogeneous visual metrics like PSNR and SSIM that correlate poorly with downstream diagnostic accuracy' and §3.4: the preprocessing phase 'remains a black box.' These are limitations of the field and of the review's evidence base, not self-consistency loops. The tri-axial framing is asserted in §1 ('the continuous, tri-axial interplay') and then echoed in the conclusion, which is a rhetorical/structural feature of any survey organized along those axes; however, this does not constitute a specific reduction of a prediction to its inputs, a fitted-input-called-prediction step, or a load-bearing self-citation. Performance tables (Tables 2, 8–10) are transcribed from ~250 external papers, not generated by the review, and no equation equates any output to an input by construction. Under the hard rule requiring quoted evidence of a specific reduction, no circular step can be exhibited. The appropriate finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The review's argument depends on three kinds of unpaid-for premises: its own organizing frame (the tri-axial co-evolution and its periodization, introduced in §1/Figure 1 and asserted, not derived); the fidelity of its transcriptions of external datasets and metrics (Tables 1–10, Appendices A–H); and anonymous 'publicly available documentation' used for critical claims about annotation practices (§2.1.3). There are no free parameters and no invented entities: the paper fits no data and postulates nothing. The frame is the main cost: it guarantees the shape of the conclusion.

assumptions (4)

ad hoc to paper Progress in CFP AI is structured by the three axes of datasets, preprocessing, and modeling, which co-evolve in the staged periods the review defines (pre-2014, 2015–2020, 2021–present).
Introduced in §1 and Figure 1 as the organizing frame; the periodization is imposed by the authors and not derived from any analysis. The entire narrative depends on it; alternative drivers (compute, benchmarks, regulation) are not considered.
domain assumption Performance metrics and dataset attributes transcribed in Tables 1–10 accurately represent the cited original publications.
The review does not audit the original papers; trend claims (e.g., 'SSMs establish a new performance ceiling', §4.3) rest on these transcriptions, and no protocol-mismatch caveats are given when metrics from heterogeneous studies are juxtaposed.
domain assumption Stage boundaries mark genuine paradigm shifts rather than merely chronological bins.
The claim that 2015–2020 was a CNN-driven data-expansion era and 2021+ a multimodal foundation era is a narrative assertion (§2.1), not derived from quantitative data on dataset/model/preprocessing couplings.
domain assumption Claims based on 'publicly available documentation' about dataset annotation practices are reliable.
§2.1.3 asserts that 'a substantial majority of recent cohorts only state in principle that their annotations adhere to widely accepted clinical conventions' without citing the specific documentation; the critical assessment of the field rests on this unverifiable generalization.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI." pith.science (2026). https://pith.science/paper/2ZJNRE5N

@misc{pith2026260723972,
  author       = {Pith},
  title        = {Pith review of: Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2ZJNRE5N}},
  note         = {Machine review of arXiv:2607.23972}
}

read the original abstract

Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence. This review provides an integrated overview of CFP AI through the interplay of dataset evolution, preprocessing paradigms, and modeling frameworks. We show that CFP datasets have evolved from small single-center collections with task-specific labels to large multi-center resources featuring multimodal pairings and longitudinal clinical records. Preprocessing has progressed from conventional image enhancement to neural data-engineering pipelines, hardware-aware token optimization, and self-supervised imputation for incomplete electronic health records (EHRs). Meanwhile, modeling has advanced from convolutional neural networks (CNNs) to vision foundation models, state space models (SSMs), and multimodal expert architectures. At the multimodal frontier, CFP is increasingly integrated with EHRs and longitudinal patient information, enabling more comprehensive clinical reasoning beyond isolated image analysis. We conclude that future progress depends on the collaborative optimization of datasets, preprocessing, and multimodal modeling, providing a roadmap toward robust clinical deployment, improved cross-domain generalization, and resource-efficient edge intelligence.

Figures

Figures reproduced from arXiv: 2607.23972 by the authors.

**Figure 1.** Evolution of Fundus Datasets, Preprocessing, and Algorithm. However, practical challenges in CFP analysis—including low contrast, uneven illumination, color distortion, tissue occlusion, and specular reflection artifacts—often compromise the stability of automated analytical pipelines and the robustness of diagnostic models [10–12]. To surmount these obstacles, researchers have devoted substantial efforts to advanci… view at source ↗

**Figure 2.** Evolution of Fundus Datasets. preprocessing approaches, and hybrid preprocessing strategies that integrate traditional and deep learning techniques; the characteristics and limitations of each category are analyzed, and their impacts on classification, detection, and segmentation tasks are evaluated. Section 4 reviews the evolution of algorithmic models, ranging from early machine learning methods to the application… view at source ↗

**Figure 3.** Example images from the DRIVE dataset [28]. (a) Original RGB fundus image; (b) Manual vessel annotation by Expert 1; (c) Manual vessel annotation by Expert 2. tens of thousands of samples, though localized small- and medium-scale cohorts continued to coexist for specialized downstream verification. In terms of task taxonomies, these intermediate-stage datasets primarily prioritized image-level classification and sta… view at source ↗

Figures from the paper (15 more)

**Figure 4.** Figure 4: Example of foveal center detection in the IDRiD dataset [16]. (a) Original; (b) Annotated [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: Examples from the EyePACS dataset [46]. (a) No DR; (b) Mild; (c) Moderate; (d) Severe; (e) PDR (Proliferative DR). To dismantle the systemic information bottlenecks and visual isolation inherent to single-modality clinical reasoning, the contemporary frontier has witne…

**Figure 6.** Figure 6: Examples of fundus image classification in the OIA-ODIR datase [50]. (a) Normal; (b) Pathological Myopia; (c) Cataract; (d) DR; (e) AMD; (f) Central Retinal Artery Occlusion; (g) Branch Retinal Artery Occlusion; (h) Glaucoma; (i) HR. recent cohorts only state in princi…

**Figure 7.** Figure 7: Examples of multimodal data in the Eyecare-100K dataset [19]. (a) UBM; (b) CT; (c) FFA; (d) ICGA; (e) Fundus; (f) OCT; (g) Slit-Lamp; (h) Fluorescein staining [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]

**Figure 8.** Figure 8: Examples of image–text pairs in the Eyecare-100K dataset [19]. 2.2. Limitations and Future Directions An analysis of these three stages indicates that retinal fundus image datasets have undergone substantial technological advancement and task expansion. From early-sta…

**Figure 9.** Figure 9: Evolution of preprocessing methods for fundus color images. for fundus color images have undergone a progressive transition from hand-crafted, rule-driven techniques to datadriven approaches, recently to hybrid frameworks that integrate both paradigms, and ultimately …

**Figure 10.** Figure 10: Comparison of methods for adjusting contrast and brightness effects. (a) Original image; (b) HE; (c) AHE; (d) ESIHE; (e) Green channel; (f) Normalization; (g) CLAHE; (h) R-CLAHE. Among nonlinear filtering methods, median filtering [22] is effective in removing impulse…

**Figure 11.** Figure 11: Comparison of smoothing and denoising methods. (a) Original image; (b) Mean filter; (c) Gaussian filter; (d) Wiener filter; (e) Median filter; (f) Weighted median filter; (g) Adaptive median filter; (h) Anisotropic diffusion filter; (i) Gabor filter; (j) Stationary wa…

**Figure 12.** Figure 12: Comparison of structure and edge enhancement methods. (a) Original image; (b) LoG; (c) Canny operator; (d) Multiscale vessel enhancement based on the Hessian matrix; (e) Embossing. elevations and depressions in the retinal posterior pole [91]. However, these tradition…

**Figure 13.** Figure 13: Illustration of Deep Learning–Assisted Preprocessing Methods. These hybrid strategies partially compensate for the limitations of individual preprocessing methods by jointly improving contrast enhancement, noise suppression, and structural preservation. However, thei…

**Figure 14.** Figure 14: Evolution of CFP Analysis Algorithm. Y. Li et al.: Preprint submitted to Elsevier Page 22 of 77 [PITH_FULL_IMAGE:figures/full_fig_p022_14.png]

**Figure 15.** Figure 15: Knowledge-driven CFP Analysis Pipeline. 4.1. Knowledge-Driven Methods Prior to the widespread adoption of deep learning approaches, CFP analysis predominantly relied on knowledgedriven analytical paradigms, as summarized in previous surveys and reviews [7, 142, 143].…

**Figure 16.** Figure 16: CNN-based End-to-end Representation Learning for CFP Analysis. backbone architectures for subsequent CFP analysis tasks, including disease classification, lesion analysis, and structure segmentation [164, 165]. Scale Modeling and Multi-Scale Feature Fusion Once CNN ba…

**Figure 17.** Figure 17: Vision-Based Unimodal Global Modeling Methods. information. The resulting token sequence is then fed into a Transformer encoder, where multi-head self-attention explicitly computes the relationships between any given token and all others, enabling the integration of l…

**Figure 18.** Figure 18: Multimodal Modeling Framework for CFP Analysis. the data dimension toward text-paired and EHR-integrated ocular cohorts, as exemplified by the report-paired corpora in RET-CLIP [202] and EyeCLIP [203], and the longitudinal clinical risk factors in DeepDR Plus [139]. I…

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

300 extracted references · 1 canonical work pages

[143]

K. B. Khan, A. A. Khaliq, A. Jalil, M. A. Iftikhar, N. Ullah, M. W. Aziz, K. Ullah, M. Shahid, A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends, Pattern Analysis and Applications 22 (2019) 767–802

2019
[1]

M.Gupta,S.Gupta,G.Palanisamy,J.S.Nisha,V.Goutham,S.ArunKumar,K.Gavaskar,G.R.Naik, Acomprehensivesurveyondetection of ocular and non-ocular diseases using color fundus images, IEEE Access 12 (2024) 194296–194321

2024
[2]

Health, Visual impairment and blindness, 2015

Organization, W. Health, Visual impairment and blindness, 2015. URL:https://web.archive.org/web/20150512062236/http: //www.who.int/mediacentre/factsheets/fs282/en/

arXiv 2015
[3]

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, C.-Y. Cheng, Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis, Ophthalmology 121 (2014) 2081–2090
[4]

W.L.Wong,X.Su,X.Li,C.M.G.Cheung,R.Klein,C.-Y.Cheng,T.Y.Wong, Globalprevalenceofage-relatedmaculardegenerationand disease burden projection for 2020 and 2040: a systematic review and meta-analysis, The Lancet Global Health 2 (2014) e106–e116

2020
[5]

Grzybowski, K

A. Grzybowski, K. Jin, J. Zhou, X. Pan, M. Wang, J. Ye, T. Y. Wong, Retina fundus photograph-based artificial intelligence algorithms in medicine: A systematic review, Ophthalmol Ther 13 (2024) 2125–2149

2024
[6]

Zhang, X

K. Zhang, X. Liu, J. Xu, J. Yuan, W. Cai, T. Chen, K. Wang, Y. Gao, S. Nie, X. Xu, et al., Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images, Nat Biomed Eng 5 (2021) 533–545

2021
[7]

Besenczi, J

R. Besenczi, J. Tóth, A. Hajdu, A review on automatic analysis techniques for color fundus photographs, Comput. Struct. Biotechnol. J. 14 (2016) 371–384

2016

Show all 300 references

[8]

Nagiel, R

A. Nagiel, R. A. Lalane, S. R. Sadda, S. D. Schwartz, Ultra-widefield fundus imaging: a review of clinical applications and future trends, Retina 36 (2016) 660–678

2016
[9]

Ophthalmol

M.K.Bhardwaj,S.Stratton,S.Roh,J.Luna,P.R.Cotran,D.J.Ramsey, Smartphone-basednonmydriaticfundusimagingtodetectdiabetic retinopathy, Invest. Ophthalmol. Vis. Sci. 65 (2024) 1761–1761

2024
[10]

König, P

M. König, P. Seeböck, B. S. Gerendas, G. Mylonas, R. Winklhofer, I. Dimakopoulou, U. M. Schmidt-Erfurth, Quality assessment of color fundus and fluorescein angiography images using deep learning, British Journal of Ophthalmology 108 (2024) 98–104

2024
[11]

C. Shi, J. Lee, G. Wang, X. Dou, F. Yuan, B. Zee, Assessment of image quality on color fundus retinal images using the automatic retinal image analysis, Sci Rep 12 (2022) 10455

2022
[12]

P.Bindhya,C.Jegan,V.Raj, Areviewonmethodsofenhancementanddenoisinginretinalfundusimages, InternationalJournalofComputer Science and Engineering 8 (2020) 1–9

2020
[13]

Hoover, M

A. Hoover, M. Goldbaum, Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels, IEEE Trans Med Imaging 22 (2003) 951–958

2003
[14]

Hoover, V

A. Hoover, V. Kouznetsova, M. Goldbaum, Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical imaging 19 (2000) 203–210

2000
[15]

E.Decencière,X.Zhang,G.Cazuguel,B.Lay,B.Cochener,C.Trone,P.Gain,J.-R.Ordóñez-Varela,P.Massin,A.Erginay,etal., Feedback on a publicly distributed image database the messidor database, Image Analysis & Stereology 33 (2014) 231–234

2014
[16]

Porwal, S

P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, F. Meriaudeau, Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research, Data 3 (2018) 25

2018
[17]

Z. Qin, Y. Yin, D. Campbell, X. Wu, K. Zou, N. Liu, Y. C. Tham, X. J. Zhang, Q. Chen, Lmod: A large multimodal ophthalmology dataset and benchmark for large vision-language models, in: Findings of the Association for Computational Linguistics: NAACL 2025, 2025, pp. 2501–2522

2025
[18]

R.Kiefer,M.Abid,J.Steen,M.R.Ardali,E.Amjadian, Acatalogofpublicglaucomadatasetsformachinelearningapplications:Adetailed descriptionandanalysisofpublicglaucomadatasetsavailabletomachinelearningengineerstacklingglaucoma-relatedproblemsusingretinal fundusimagesandoctimages., in:P...

2023
[19]

S. Li, T. Lin, L. Lin, W. Zhang, J. Liu, X. Yang, J. Li, Y. He, X. Song, J. Xiao, et al., Eyecaregpt: Boosting comprehensive ophthalmology understandingwithtailoreddataset,benchmarkandmodel, in:Proceedingsofthe33rdACMInternationalConferenceonMultimedia,2025, pp. 3893–3902

2025
[20]

F.Shang,J.Fu,Y.Yang,H.Huang,J.Liu,L.Ma, Synfundus:Asyntheticfundusimagesdatasetwithmillionsofsamplesandmulti-disease annotations, arXiv preprint arXiv:2312.00377 3 (2023)

2023 arXiv
[21]

O.Sule,S.Viriri, Contrastenhancementofrgbretinalfundusimagesforimprovedsegmentationofbloodvesselsusingconvolutionalneural networks, J Digit Imaging 36 (2023) 414–432

2023
[22]

Huang, G

T. Huang, G. Yang, G. Tang, A fast two-dimensional median filtering algorithm, IEEE Transactions on Acoustics, Speech, and Signal Processing 27 (1979) 13–18

1979
[23]

Sadok, M

Z. Sadok, M. Akil, R. Kachouri, A. Ahaitouf, Diabetic retinopathy screening within unlabeled dataset based on least squares cycle-gan domain transfer, in: 2024 IEEE Thirteenth International Conference on Image Processing Theory, Tools and Applications (IPTA), IEEE, 2024, pp. 1–7

2024
[24]

T. K. Yoo, J. Y. Choi, H. K. Kim, Cyclegan-based deep learning technique for artifact reduction in fundus photography, Graefes Arch Clin Exp Ophthalmol 258 (2020) 1631–1637

2020
[25]

N. J. Mohan, R. Murugan, T. Goel, P. Roy, Fast and robust exudate detection in retinal fundus images using extreme learning machine autoencoders and modified kaze features, J Digit Imaging 35 (2022) 496–513

2022
[26]

W. T. Song, I.-C. Lai, Y.-Z. Su, A statistical robust glaucoma detection framework combining retinex, cnn, and doe using fundus images, IEEE Access 9 (2021) 103772–103783

2021
[27]

X. Wang, D. Gong, Y. Chen, Z. Zong, M. Li, K. Fan, L. Jia, Q. Cao, Q. Liu, Q. Yang, Hybrid cnn-mamba model for multi-scale fundus image enhancement, Biomed Opt Express 16 (2025) 1104–1117

2025
[28]

Staal, M

J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, B. Van Ginneken, Ridge-based vessel segmentation in color images of the retina, IEEE Trans Med Imaging 23 (2004) 501–509. Y. Li et al.:Preprint submitted to ElsevierPage 68 of 77

2004
[29]

A. R. Vittorino, G. Immanuel, S. Y. Prasetyo, E. S. Purwanto, Machine learning approaches for diabetic retinopathy classification utilizing gabor, lbp, and hog feature extraction, in: 2024 Beyond Technology Summit on Informatics International Conference (BTS-I2C), IEEE, 2024, ...

2024
[30]

T.Shyamalee,D.Meedeniya, Attentionu-netforglaucomaidentificationusingfundusimagesegmentation, in:2022internationalconference on decision aid sciences and applications (DASA), IEEE, 2022, pp. 6–10

2022
[31]

Lin, K.-C

C.-L. Lin, K.-C. Wu, Development of revised resnet-50 for diabetic retinopathy detection, BMC Bioinformatics 24 (2023) 157

2023
[32]

R. Fan, K. Alipour, C. Bowd, M. Christopher, N. Brye, J. A. Proudfoot, M. H. Goldbaum, A. Belghith, C. A. Girkin, M. A. Fazio, et al., Detecting glaucoma from fundus photographs using deep learning without convolutions: Transformer for improved generalization, Ophthalmol Sci 3...

2023
[33]

R. B. Bhardwaj, D. A. Haneef, Use of segment anything model (sam) and medsam in the optic disc segmentation of colour retinal fundus images: experimental finding, Indian J. Health Care Med. Pharm. Pract. 4 (2023) 82–93

2023
[34]

Z. Deng, W. Gao, C. Chen, Z. Niu, Z. Gong, R. Zhang, Z. Cao, F. Li, Z. Ma, W. Wei, L. Ma, Ophglm: An ophthalmology large language- and-vision assistant, Artificial Intelligence in Medicine 157 (2024) 103001

2024
[35]

D. Shi, W. Zhang, J. Yang, S. Huang, X. Chen, M. Yusufu, K. Jin, S. Lin, S. Liu, Q. Zhang, et al., Eyeclip: A visual–language foundation model for multi-modal ophthalmic image analysis, arXiv preprint arXiv:2409.06644 (2024)

2024 arXiv
[36]

M.M.Fraz,P.Remagnino,A.Hoppe,B.Uyyanonvara,A.R.Rudnicka,C.G.Owen,S.A.Barman,Bloodvesselsegmentationmethodologies in retinal images–a survey, Comput Methods Programs Biomed 108 (2012) 407–433

2012
[37]

Image Anal

G.Litjens,T.Kooi,B.E.Bejnordi,A.A.A.Setio,F.Ciompi,M.Ghafoorian,J.A.VanDerLaak,B.VanGinneken,C.I.Sánchez, Asurvey on deep learning in medical image analysis, Med. Image Anal. 42 (2017) 60–88

2017
[38]

T.Li,W.Bo,C.Hu,H.Kang,H.Liub,K.Wanga,H.Fuc, Applicationsofdeeplearninginfundusimages:Areview, MedicalImageAnalysis 69 (2021) 101971

2021
[39]

Sheng, H

H. Sheng, H. Du, X. Shen, S. Wang, X. Yu, Multimodal retina image analysis survey: datasets, tasks and methods, in: Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025, pp. 10650–10659

2025
[40]

T.Dharmaseelan,N.Sinha,S.Ashraf,K.Daneshvar,Y.W.Chan,N.Pontikos,Aninvestigativestudyofmethodsforretinalimageregistration, medRxiv (2025) 2025–12

2025
[41]

Kauppi, V

T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, H. Uusitalo, H. Kälviäinen, J. Pietilä, Diaretdb0: Evaluation database andmethodologyfordiabeticretinopathyalgorithms, MachineVisionandPatternRecognitionResearchGroup,LappeenrantaUniversityof Technology, Finland...

2006
[42]

Kauppi, J.-K

T. Kauppi, J.-K. Kämäräinen, L. Lensu, V. Kalesnykiene, I. Sorri, H. Uusitalo, H. Kälviäinen, Constructing benchmark databases and protocols for medical image analysis: diabetic retinopathy, Computational and Mathematical Methods in Medicine 2013 (2013) 368514

2013
[43]

Kälviäinen, H

R. Kälviäinen, H. Uusitalo, Diaretdb1 diabetic retinopathy database and evaluation protocol, in: Medical image understanding and analysis, volume 2007, Citeseer, 2007, p. 61

2007
[44]

URL:https://datasetninja.com/chase-db1

Child Heart and Health Study in England (CHASE), Chase-db1 dataset, 2012. URL:https://datasetninja.com/chase-db1

2012
[45]

A.Budai,R.Bock,A.Maier,J.Hornegger,G.Michelson, Robustvesselsegmentationinfundusimages, InternationalJournalofBiomedical Imaging 2013 (2013) 154860

2013
[46]

URL:https://www.kaggle.com/c/diabetic-retinopathy-detection/ data

Kaggle, Diabetic retinopathy detection (eyepacs), 2015. URL:https://www.kaggle.com/c/diabetic-retinopathy-detection/ data

2015
[47]

Hernandez-Matas, X

C. Hernandez-Matas, X. Zabulis, A. Triantafyllou, P. Anyfanti, S. Douma, A. A. Argyros, Fire: fundus image registration dataset, J. Model. Ophthalmol. 1 (2017) 16–28

2017
[48]

S. Holm, G. Russell, V. Nourrit, N. McLoughlin, Dr hagis—a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients, Journal of Medical Imaging 4 (2017) 014503

2017
[49]

URL:https://www.kaggle.com/c/aptos2019-blindness-detection/data

Kaggle, Aptos 2019 blindness detection, 2019. URL:https://www.kaggle.com/c/aptos2019-blindness-detection/data

2019
[50]

N. Li, T. Li, C. Hu, K. Wang, H. Kang, A benchmark of ocular disease intelligent recognition: One shot for multi-disease detection, in: International symposium on benchmarking, measuring and optimization, Springer, 2020, pp. 177–193

2020
[51]

De Vente, K

C. De Vente, K. A. Vermeer, N. Jaccard, H. Wang, H. Sun, F. Khader, D. Truhn, T. Aimyshev, Y. Zhanibekuly, T.-D. Le, et al., Airogs: Artificial intelligence for robust glaucoma screening challenge, IEEE Trans Med Imaging 43 (2024) 542–557

2024
[52]

J.Wu,H.Fang,F.Li,H.Fu,F.Lin,J.Li,Y.Huang,Q.Yu,S.Song,X.Xu,etal., Gammachallenge:glaucomagradingfrommulti-modality images, Medical Image Analysis 90 (2023) 102938

2023
[53]

Bidwai, S

P. Bidwai, S. Gite, A. Gupta, K. Pahuja, K. Kotecha, Multimodal dataset using octa and fundus images for the study of diabetic retinopathy, Data in Brief 52 (2024) 110033

2024
[54]

Y.Zhou,M.A.Chia,S.K.Wagner,M.S.Ayhan,D.J.Williamson,R.R.Struyven,T.Liu,M.Xu,M.G.Lozano,P.Woodward-Court,etal., A foundation model for generalizable disease detection from retinal images, Nature 622 (2023) 156–163

2023
[55]

J. Qiu, J. Wu, H. Wei, P. Shi, M. Zhang, Y. Sun, L. Li, H. Liu, H. Liu, S. Hou, et al., Visionfm: A multi-modal multi-task vision foundation model for generalist ophthalmic artificial intelligence, arXiv preprint arXiv:2310.04992 (2023)

2023 arXiv
[56]

Conquer, T

V. Conquer, T. Lambolais, G. Andrade-Miranda, B. Magnier, Comprehensive review of open-source fundus image databases for diabetic retinopathy diagnosis, Sensors 25 (2025) 5658

2025
[57]

H. Fang, F. Li, H. Fu, X. Sun, X. Cao, F. Lin, J. Son, S. Kim, G. Quellec, S. Matta, et al., Adam challenge: Detecting age-related macular degeneration from fundus images, IEEE Transactions on Medical Imaging 41 (2022) 2828–2847

2022
[58]

M.Niemeijer,B.VanGinneken,M.J.Cree,A.Mizutani,G.Quellec,C.I.Sánchez,B.Zhang,R.Hornero,M.Lamard,C.Muramatsu,etal., Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs, IEEE Trans Med Imaging 29 (2009) 185–195

2009
[59]

M. D. Abramoff, W. L. Alward, E. C. Greenlee, L. Shuba, C. Y. Kim, J. H. Fingert, Y. H. Kwon, Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features, Invest. Ophthalmol. Vis. Sci. 48 (2007) 1665–1673. Y. Li et al.:Prepri...

2007
[60]

S. M. Khan, X. Liu, S. Nath, E. Korot, L. Faes, S. K. Wagner, P. A. Keane, N. J. Sebire, M. J. Burton, A. K. Denniston, A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability, The Lancet Digital Health 3...

2021
[61]

Z. Li, L. Wang, X. Wu, J. Jiang, W. Qiang, H. Xie, H. Zhou, S. Wu, Y. Shao, W. Chen, Artificial intelligence in ophthalmology: the path to the real-world clinic, Cell Reports Medicine 4 (2023)

2023
[62]

Popovic, S

N. Popovic, S. Vujosevic, M. Radunović, M. Radunović, T. Popovic, Trend database: Retinal images of healthy young subjects visualized by a portable digital non-mydriatic fundus camera, PLoS One 16 (2021) e0254918

2021
[63]

Vamsidhar, S

D. Vamsidhar, S. Kolhar, S. Patil, S. Kumar, Advancements in ophthalmology healthcare using multimodal ai: a systematic review of methods, applications, and future directions, Discover Artificial Intelligence (2026)

2026
[64]

X. Li, W. L. Wong, C. Y.-l. Cheung, C.-Y. Cheng, M. K. Ikram, J. Li, K. S. Chia, T. Y. Wong, Racial differences in retinal vessel geometric characteristics: a multiethnic study in healthy asians, Invest Ophthalmol Vis Sci 54 (2013) 3650–3656

2013
[65]

M. I. Seider, R. Y. Lee, D. Wang, M. Pekmezci, T. C. Porco, S. C. Lin, Optic disk size variability between african, asian, white, hispanic, and filipino americans using heidelberg retinal tomography, J Glaucoma 18 (2009) 595–600

2009
[66]

M.Y.Yip,G.Lim,Z.W.Lim,Q.D.Nguyen,C.C.Chong,M.Yu,V.Bellemo,Y.Xie,X.Q.Lee,H.Hamzah,etal., Technicalandimaging factors influencing performance of deep learning systems for diabetic retinopathy, NPJ Digit Med 3 (2020) 40

2020
[67]

Y. Yi, D. Zhang, Observation model based retinal fundus image normalization and enhancement, in: 2011 4th International Congress on Image and Signal Processing, volume 2, IEEE, 2011, pp. 719–723

2011
[68]

S. K. Yadav, S. Kumar, B. Kumar, R. Gupta, Comparative analysis of fundus image enhancement in detection of diabetic retinopathy, in: 2016 IEEE region 10 humanitarian technology conference (R10-HTC), IEEE, 2016, pp. 1–5. doi:10.1109/r10-htc.2016.7906814

2016
[69]

Tavakoli, F

M. Tavakoli, F. Kalantari, A. Golestaneh, Comparing different preprocessing methods in automated segmentation of retinal vasculature, in: 2017 IEEE Nuclear science symposium and medical imaging conference (NSS/MIC), IEEE, 2017, pp. 1–8

2017
[70]

M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. S. Lim, E. Y. K. Ng, A. Laude, Computer-aided diagnosis of diabetic retinopathy: A review, Computers in Biology and Medicine 43 (2013) 2136–2155

2013
[71]

S. B. Sayadia, Y. Elloumi, R. Kachouri, M. Akil, A. B. Abdallah, M. H. Bedoui, Automated method for real-time amd screening of fundus images dedicated for mobile devices, Medical & Biological Engineering & Computing 60 (2022) 1449–1479

2022
[72]

S.Zhang,C.A.Webers,T.T.Berendschot, Computationalsinglefundusimagerestorationtechniques:areview, FrontiersinOphthalmology 4 (2024) 1332197

2024
[73]

S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. ter Haar Romeny, J. B. Zimmerman, K. Zuiderveld, Adaptive histogram equalization and its variations, Computer vision, graphics, and image processing 39 (1987) 355–368

1987
[74]

Joshi, P

S. Joshi, P. Karule, Review of preprocessing techniques for fundus image analysis, Adv Model Anal B 60 (2017) 593–612

2017
[75]

Jintasuttisak, S

T. Jintasuttisak, S. Intajag, Color retinal image enhancement by rayleigh contrast-limited adaptive histogram equalization, in: 2014 14th international conference on control, automation and systems (ICCAS 2014), IEEE, 2014, pp. 692–697

2014
[76]

thesis, Université de Bourgogne, 2011

Giancardo, Luca, Automated fundus images analysis techniques to screen retinal diseases in diabetic patients, Ph.D. thesis, Université de Bourgogne, 2011

2011
[77]

Narasimha-Iyer, A

H. Narasimha-Iyer, A. Can, B. Roysam, V. Stewart, H. L. Tanenbaum, A. Majerovics, H. Singh, Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy, IEEE Trans Biomed Eng 53 (2006) 1084–1098

2006
[78]

W.Nazih,A.O.Aseeri,O.Y.Atallah,S.El-Sappagh, Visiontransformermodelforpredictingtheseverityofdiabeticretinopathyinfundus photography-based retina images, IEEE Access 11 (2023) 117546–117561

2023
[79]

Jähne, Digital image processing, Springer, 2005

B. Jähne, Digital image processing, Springer, 2005

2005
[80]

Mayya, S

V. Mayya, S. K. S, U. Kulkarni, D. K. Surya, U. R. Acharya, An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images, Appl Intell (Dordr) 53 (2023) 1548–1566

2023
[81]

Ramya, R

V. Ramya, R. Jayaparvathy, Intensified u-net architecture for segmentation of diabetic retinopathy in retinal image processing, Traitement du Signal 42 (2025) 1685–1695

2025
[82]

Hwang, R

H. Hwang, R. A. Haddad, Adaptive median filters: new algorithms and results, IEEE Transactions on Image Processing 4 (1995) 499–502

1995
[83]

S. S. Manek, H. Tjandrasa, Metode soft weighted median filter untuk perbaikan segmentasi citra dengan noise, 2018

2018
[84]

Gayathri, S

S. Gayathri, S. Joseph Jawhar, Enhancement in the vision of branch retinal artery occluded images using boosted anisotropic diffusion filter – an ophthalmic assessment, IETE Journal of Research 68 (2020) 2707–2715

2020
[85]

Hayashi, T

Y. Hayashi, T. Nakagawa, Y. Hatanaka, A. Aoyama, M. Kakogawa, T. Hara, H. Fujita, T. Yamamoto, Detection of retinal nerve fiber layer defectsinretinalfundusimagesusinggaborfiltering, in:MedicalImaging2007:Computer-AidedDiagnosis,volume6514,SPIE,2007,pp. 936–943

2007
[86]

Bekkers, R

E. Bekkers, R. Duits, T. Berendschot, B. ter Haar Romeny, A multi-orientation analysis approach to retinal vessel tracking, Journal of Mathematical Imaging and Vision 49 (2014) 583–610

2014
[87]

A.F.M.Hani,T.A.Soomro,I.Faye,N.Kamel,N.Yahya, Denoisingmethodsforretinalfundusimages, in:20145thinternationalconference on intelligent and advanced systems (ICIAS), IEEE, 2014, pp. 1–6

2014
[88]

R. Dhar, R. Gupta, K. Baishnab, An analysis of canny and laplacian of gaussian image filters in regard to evaluating retinal image, in: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), IEEE, 2014, pp. 1–6

2014
[89]

A. D. Mayangsari, I. W. P. Agung, A systematic literature review: Performance comparison of edge detection operators in medical images, Jurnal ELTIKOM: Jurnal Teknik Elektro, Teknologi Informasi dan Komputer 8 (2024) 9–25

2024
[90]

X. Yin, B. W. Ng, J. He, Y. Zhang, D. Abbott, Accurate image analysis of the retina using hessian matrix and binarisation of thresholded entropy with application of texture mapping, PLoS One 9 (2014) e95943

2014
[91]

A.M.Kolomeyer,B.C.Szirth,K.S.Shahid,G.Pelaez,N.V.Nayak,A.S.Khouri, Software-assistedanalysisduringocularhealthscreening, Telemedicine and e-Health 19 (2013) 2–6. Y. Li et al.:Preprint submitted to ElsevierPage 70 of 77

2013
[92]

B.Gupta,M.Tiwari, Colorretinalimageenhancementusingluminosityandquantilebasedcontrastenhancement, MultidimensionalSystems and Signal Processing 30 (2019) 1829–1837

2019
[93]

M.Zhou,K.Jin,S.Wang,J.Ye,D.Qian, Colorretinalimageenhancementbasedonluminosityandcontrastadjustment, IEEETransBiomed Eng 65 (2018) 521–527

2018
[94]

Desiani, M

A. Desiani, M. Adrezo, A. M. Alfan, B. Suprihatin, et al., A hybrid system for enhancement retinal image reduction, in: 2021 International ConferenceonInformatics,Multimedia,CyberandInformationSystem(ICIMCIS),2021,pp.80–85.doi:10.1109/icimcis53775.2021. 9699259

2021
[95]

Subudhi, S

A. Subudhi, S. Pattnaik, S. Sabut, Blood vessel extraction of diabetic retinopathy using optimized enhanced images and matched filter, Journal of Medical Imaging 3 (2016) 044003–044003

2016
[96]

Kumar, S

S. Kumar, S. Choudhary, R. Gupta, B. Kumar, Performance evaluation of joint filtering and histogram equalization techniques for retinal fundus image enhancement, in: 2018 5th IEEE Uttar Pradesh section international conference on electrical, electronics and computer engineerin...

2018
[97]

R.D.Badgujar,P.J.Deore,Regiongrowingbasedsegmentationusingforstnercornerdetectiontheoryforaccuratemicroaneurysmsdetection in retinal fundus images, in: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), IEEE, 2018, pp. 1–5. doi:10...

2018
[98]

Naveed, F

K. Naveed, F. Daud, H. A. Madni, M. A. Khan, T. M. Khan, S. S. Naqvi, Towards automated eye diagnosis: An improved retinal vessel segmentation framework using ensemble block matching 3d filter, Diagnostics 11 (2021) 114

2021
[99]

Khawaja, T

A. Khawaja, T. M. Khan, K. Naveed, S. S. Naqvi, N. U. Rehman, S. Junaid Nawaz, An improved retinal vessel segmentation framework using frangi filter coupled with the probabilistic patch based denoiser, IEEE Access 7 (2019) 164344–164361

2019
[100]

J. Lin, J. Zheng, B. Lin, A review of deep learning for fundus image enhancement, Discover Computing 28 (2025) 233

2025
[101]

L. Ye, X. Fu, A. Liu, Z.-J. Zha, A decomposition-based network for non-uniform illuminated retinal image enhancement, in: 2021 15th International Symposium on Medical Information and Communication Technology (ISMICT), IEEE, 2021, pp. 59–64. doi:10.1109/ ismict51748.2021.9434912

2021
[102]

Y.Jia,G.Chen,H.Chi, Retinalfundusimagesuper-resolutionbasedongenerativeadversarialnetworkguidedwithvascularstructureprior, Scientific Reports 14 (2024) 22786

2024
[103]

Y.Ma,J.Liu,Y.Liu,H.Fu,Y.Hu,J.Cheng,H.Qi,Y.Wu,J.Zhang,Y.Zhao, Structureandilluminationconstrainedganformedicalimage enhancement, IEEE Transactions on Medical Imaging 40 (2021) 3955–3967

2021
[104]

W. Zhu, P. Qiu, O. M. Dumitrascu, J. M. Sobczak, M. Farazi, Z. Yang, K. Nandakumar, Y. Wang, Otre: Where optimal transport guided unpaired image-to-image translation meets regularization by enhancing, in: International Conference on Information Processing in Medical Imaging, S...

2023
[105]

W. Zhu, P. Qiu, M. Farazi, K. Nandakumar, O. M. Dumitrascu, Y. Wang, Optimal transport guided unsupervised learning for enhancing low-quality retinal images, in: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), IEEE, 2023, pp. 1–5

2023
[106]

Mahapatra, B

D. Mahapatra, B. Bozorgtabar, R. Garnavi, Image super-resolution using progressive generative adversarial networks for medical image analysis, Computerized Medical Imaging and Graphics 71 (2019) 30–39

2019
[107]

K. G. Lee, S. J. Song, S. Lee, H. G. Yu, D. I. Kim, K. M. Lee, A deep learning-based framework for retinal fundus image enhancement, PLoS One 18 (2023) e0282416

2023
[108]

Z.Shen,H.Fu,J.Shen,L.Shao, Modelingandenhancinglow-qualityretinalfundusimages, IEEETransMedImaging40(2021)996–1006

2021
[109]

E. E. Pazo, S. Moutari, F. Gao, L. Hu, M. Usama, X. Li, J. Liu, Structure-preserving super-resolution of retinal fundus images via a dual-transformer residual network, Frontiers in Medicine 12 (2026) 1730678

2026
[110]

H.Li,H.Liu,Y.Hu,H.Fu,Y.Zhao,H.Miao,J.Liu, Anannotation-freerestorationnetworkforcataractousfundusimage, IEEETransactions on Medical Imaging 41 (2022) 1699–1710

2022
[111]

V. K. S. Vasa, P. Qiu, W. Zhu, Y. Xiong, O. Dumitrascu, Y. Wang, Context-aware optimal transport learning for retinal fundus image enhancement, in: Proceedings of the Winter Conference on Applications of Computer Vision, 2025, pp. 4016–4025

2025
[112]

Alwakid, W

G. Alwakid, W. Gouda, M. Humayun, Deep learning-based prediction of diabetic retinopathy using clahe and esrgan for enhancement, Healthcare 11 (2023) 863

2023
[113]

Bhoopal, M

S. Bhoopal, M. Rao, C. H. Krishnappa, Enhanced diabetic retinopathy detection and classification using fundus images with resnet50 and clahe-gan, Indonesian Journal of Electrical Engineering and Computer Science 35 (2024) 366–377

2024
[114]

K. Xu, Z. Liang, W. Wei, H. Chen, Y. Jin, Fundus image enhancement with pyramid conditional flow, IEEE Journal of Biomedical and Health Informatics 30 (2026) 413–424

2026
[115]

H.Li,H.Liu,H.Fu,Y.Xu,H.Shu,K.Niu,Y.Hu,J.Liu, Agenericfundusimageenhancementnetworkboostedbyfrequencyself-supervised representation learning, Medical Image Analysis 90 (2023) 102945

2023
[116]

Badar, M

M. Badar, M. Haris, A. Fatima, Application of deep learning for retinal image analysis: A review, Comput. Sci. Rev. 35 (2020) 100203

2020
[117]

Rezaei, S

A. Rezaei, S. Matta, R. Zeghlache, P.-H. Conze, C. Lepicard, P. Deman, L. Borderie, D. Cosette, S. Bonnin, A. Couturier, et al., Automated multimodalseverityassessmentofdiabeticretinopathyusingultra-widefieldcolorfundusphotographyandclinicaltabulardata, Biomedical Signal Proce...

2026
[118]

M. Wang, T. Lin, A. Lin, K. Yu, Y. Peng, L. Wang, C. Chen, K. Zou, H. Liang, M. Chen, et al., Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model, Nature Communications 16 (2025) 5528

2025
[119]

E. E. Hwang, D. Chen, Y. Han, L. Jia, J. Shan, Multi-dataset comparison of vision transformers and convolutional neural networks for detecting glaucomatous optic neuropathy from fundus photographs, Bioengineering 10 (2023) 1266

2023
[120]

H. Wang, Y. Chen, W. Chen, H. Xu, H. Zhao, B. Sheng, H. Fu, G. Yang, L. Zhu, Serp-mamba: Advancing high-resolution retinal vessel segmentation with selective state-space model, IEEE Transactions on Medical Imaging (2025)

2025
[121]

S.Chaudhuri,S.Chatterjee,N.Katz,M.Nelson,M.Goldbaum, Detectionofbloodvesselsinretinalimagesusingtwo-dimensionalmatched filters, IEEE Transactions on medical imaging 8 (1989) 263–269. Y. Li et al.:Preprint submitted to ElsevierPage 71 of 77

1989
[122]

Zana, J.-C

F. Zana, J.-C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE transactions on image processing 10 (2001) 1010–1019

2001
[123]

Al-Diri, A

B. Al-Diri, A. Hunter, D. Steel, An active contour model for segmenting and measuring retinal vessels, IEEE Transactions on Medical imaging 28 (2009) 1488–1497

2009
[124]

A.Salazar-Gonzalez,D.Kaba,Y.Li,X.Liu, Segmentationofthebloodvesselsandopticdiskinretinalimages, IEEEjournalofbiomedical and health informatics 18 (2014) 1874–1886

2014
[125]

J. Kaur, P. Kaur, Automated computer-aided diagnosis of diabetic retinopathy based on segmentation and classification using k-nearest neighbor algorithm in retinal images, The Computer Journal 66 (2023) 2011–2032

2023
[126]

Ricci, R

E. Ricci, R. Perfetti, Retinal blood vessel segmentation using line operators and support vector classification, IEEE transactions on medical imaging 26 (2007) 1357–1365

2007
[127]

A.Osareh,B.Shadgar, Automaticbloodvesselsegmentationincolorimagesofretina, IranianJournalofScienceandTechnology33(2009) 191

2009
[128]

J. I. Orlando, E. Prokofyeva, M. B. Blaschko, A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images, IEEE Transactions on Biomedical Engineering 64 (2016) 16–27

2016
[129]

M. M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. R. Rudnicka, C. G. Owen, S. A. Barman, An ensemble classification-based approach applied to retinal blood vessel segmentation, IEEE Trans. Biomed. Eng. 59 (2012) 2538–2548

2012
[130]

X.Yuan,L.Zhou,S.Yu,M.Li,X.Wang,X.Zheng, Amulti-scaleconvolutionalneuralnetworkwithcontextforjointsegmentationofoptic disc and cup, Artificial Intelligence in Medicine 113 (2021) 102035

2021
[131]

L. Wang, H. Liu, Y. Lu, H. Chen, J. Zhang, J. Pu, A coarse-to-fine deep learning framework for optic disc segmentation in fundus images, Biomedical Signal Processing and Control 51 (2019) 82–89

2019
[132]

Xiong, S

H. Xiong, S. Liu, R. V. Sharan, E. Coiera, S. Berkovsky, Weak label based bayesian u-net for optic disc segmentation in fundus images, Artificial Intelligence in Medicine 126 (2022) 102261

2022
[133]

Y.Zhou,Z.Chen,H.Shen,X.Zheng,R.Zhao,X.Duan, Arefinedequilibriumgenerativeadversarialnetworkforretinalvesselsegmentation, Neurocomputing 437 (2021) 118–130

2021
[134]

R. Liu, T. Wang, X. Zhang, X. Zhou, Da-res2unet: Explainable blood vessel segmentation from fundus images, Alexandria Engineering Journal 68 (2023) 539–549

2023
[135]

J. Wu, R. Hu, Z. Xiao, J. Chen, J. Liu, Vision transformer-based recognition of diabetic retinopathy grade, Medical Physics 48 (2021) 7850–7863

2021
[136]

Huang, Y

C. Huang, Y. Jiang, X. Yang, C. Wei, H. Chen, W. Xiong, H. Lin, X. Wang, T. Tian, H. Tan, Enhancing retinal fundus image quality assessment with swin-transformer–based learning across multiple color-spaces, Translational Vision Science & Technology 13 (2024) 8

2024
[137]

3955–3960

J.Yu,Y.Nie, F.Qi,W.Liao,H.Cai, Fundusam:Aspecializeddeeplearningmodelfor enhancedopticdiscandcupsegmentationinfundus images, in: 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, 2024, pp. 3955–3960

2024
[138]

Silva-Rodriguez, H

J. Silva-Rodriguez, H. Chakor, R. Kobbi, J. Dolz, I. B. Ayed, A foundation language-image model of the retina (flair): Encoding expert knowledge in text supervision, Medical Image Analysis 99 (2025) 103357

2025
[139]

L. Dai, B. Sheng, T. Chen, Q. Wu, R. Liu, C. Cai, L. Wu, D. Yang, H. Hamzah, Y. Liu, et al., A deep learning system for predicting time to progression of diabetic retinopathy, Nature Medicine 30 (2024) 584–594

2024
[140]

J.Ezembu,R.Orji,O.Oyebode,Multi-modaldatafusionwithfederatedmulti-headattentionfordiabeticretinopathyseverityclassification,
[141]

G.Zhang,J.Huo,M.J.Cardoso,T.L.Jackson,C.Bergeles,etal., Atime-seriesvision–languagemodelforpredictingprogressionofdiabetic retinopathy, in: NeurIPS 2025 Workshop on Learning from Time Series for Health, 2025

2025
[142]

Mittal, V

K. Mittal, V. M. A. Rajam, Computerized retinal image analysis-a survey, Multimed. Tools Appl. 79 (2020) 22389–22421

2020
[144]

A.F.Frangi,W.J.Niessen,K.L.Vincken,M.A.Viergever, Multiscalevesselenhancementfiltering, in:Internationalconferenceonmedical image computing and computer-assisted intervention, Springer, 1998, pp. 130–137

1998
[145]

A. M. Mendonca, A. Campilho, Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction, IEEE Trans. Med. Imaging 25 (2006) 1200–1213

2006
[146]

L. C. Rodrigues, M. Marengoni, Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and hessian-based multi-scale filtering, Biomedical signal processing and control 36 (2017) 39–49

2017
[147]

Sofka, C

M. Sofka, C. V. Stewart, Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures, IEEE transactions on medical imaging 25 (2006) 1531–1546

2006
[148]

Welfer, J

D. Welfer, J. Scharcanski, C. M. Kitamura, M. M. Dal Pizzol, L. W. Ludwig, D. R. Marinho, Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach, Computers in Biology and Medicine 40 (2010) 124–137

2010
[149]

Azzopardi, N

G. Azzopardi, N. Strisciuglio, M. Vento, N. Petkov, Trainable cosfire filters for vessel delineation with application to retinal images, Med. Image Anal. 19 (2015) 46–57

2015
[150]

M. M. Fraz, A. Basit, S. Barman, Application of morphological bit planes in retinal blood vessel extraction, Journal of digital imaging 26 (2013) 274–286

2013
[151]

Jeong, Y.-J

Y. Jeong, Y.-J. Hong, J.-H. Han, Review of machine learning applications using retinal fundus images, Diagnostics 12 (2022) 134

2022
[152]

Roychowdhury, D

S. Roychowdhury, D. D. Koozekanani, K. K. Parhi, Blood vessel segmentation of fundus images by major vessel extraction and subimage classification, IEEE journal of biomedical and health informatics 19 (2014) 1118–1128

2014
[153]

R. Bock, J. Meier, L. G. Nyúl, J. Hornegger, G. Michelson, Glaucoma risk index: automated glaucoma detection from color fundus images, Medical Image Analysis 14 (2010) 471–481. Y. Li et al.:Preprint submitted to ElsevierPage 72 of 77

2010
[154]

J. V. Soares, J. J. Leandro, R. M. Cesar, H. F. Jelinek, M. J. Cree, Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification, IEEE Transactions on Medical Imaging 25 (2006) 1214–1222

2006
[155]

J. Mo, L. Zhang, Multi-level deep supervised networks for retinal vessel segmentation, International journal of computer assisted radiology and surgery 12 (2017) 2181–2193

2017
[156]

D. S. W. Ting, C. Y.-L. Cheung, G. Lim, G. S. W. Tan, N. D. Quang, A. Gan, H. Hamzah, R. Garcia-Franco, I. Y. San Yeo, S. Y. Lee, et al., Developmentandvalidationofadeeplearningsystemfordiabeticretinopathyandrelatedeyediseasesusingretinalimagesfrommultiethnic populations with ...

2017
[157]

Pratt, F

H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, Y. Zheng, Convolutional neural networks for diabetic retinopathy, Procedia computer science 90 (2016) 200–205

2016
[158]

Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi, J. Zhong, Diagnosis of diabetic retinopathy using deep neural networks, IEEE Access 7 (2021) 3978–5154

2021
[159]

K. J. Noh, S. J. Park, S. Lee, Scale-space approximated convolutional neural networks for retinal vessel segmentation, Computer Methods and Programs in Biomedicine 178 (2019) 237–246

2019
[160]

R.Zhao,Q.Li,J.Wu,J.You, Anestedu-shapenetworkwithmulti-scaleupsampleattentionforrobustretinalvascularsegmentation, Pattern Recognition 120 (2021) 107998

2021
[161]

Z. Zhuo, J. Huang, K. Lu, D. Pan, S. Feng, A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index, Computer methods and programs in biomedicine 196 (2020) 105508

2020
[162]

Signal Process

R.Bhattacharya,R.Hussain,A.Chatterjee,D.Paul,S.Chatterjee,D.Dey,Py-net:Rethinkingsegmentationframeworkswithdensepyramidal operations for optic disc and cup segmentation from retinal fundus images, Biomed. Signal Process. Control 85 (2023) 104895

2023
[163]

L. Wang, J. Gu, Y. Chen, Y. Liang, W. Zhang, J. Pu, H. Chen, Automated segmentation of the optic disc from fundus images using an asymmetric deep learning network, Pattern Recognition 112 (2021) 107810

2021
[164]

M.Nawaz,T.Nazir,A.Javed,U.Tariq,H.-S.Yong,M.A.Khan,J.Cha, Anefficientdeeplearningapproachtoautomaticglaucomadetection using optic disc and optic cup localization, Sensors 22 (2022) 434

2022
[165]

Arrieta-Rodriguez, J

E. Arrieta-Rodriguez, J. Araque-Gallardo, N. P. Barrios, O. L. T. Forero, M. C. Bonfante, E. De-La-Hoz-Franco, M. Gamarra, J. Escorcia- Gutierrez, Deep learning for glaucoma classification and grading: A comprehensive review on fundus imaging approaches, IEEE Access (2025)

2025
[166]

7132–7141

J.Hu,L.Shen,G.Sun, Squeeze-and-excitationnetworks, in:ProceedingsoftheIEEEconferenceoncomputervisionandpatternrecognition, 2018, pp. 7132–7141

2018
[167]

M. Tan, Q. Le, Efficientnet: Rethinking model scaling for convolutional neural networks, in: International conference on machine learning, PMLR, 2019, pp. 6105–6114

2019
[168]

Mukherjee, S

N. Mukherjee, S. Sengupta, M. N. Ahmed, S. I. Yaqoob, M. R. Hussain, A. T. Zamani, Bi-directional hybrid attention feature pyramid network for detecting diabetic macular edema in retinal fundus images, IEEE access (2025)

2025
[169]

Melinscak, P

M. Melinscak, P. Prentasic, S. Loncaric, Retinal vessel segmentation using deep neural networks., in: VISAPP (1), 2015, pp. 577–582

2015
[170]

Z. Xie, T. Ling, Y. Yang, R. Shu, B. J. Liu, Optic disc and cup image segmentation utilizing contour-based transformation and sequence labeling networks, Journal of Medical Systems 44 (2020) 96

2020
[171]

Septiarini, H

A. Septiarini, H. Hamdani, E. Setyaningsih, E. Junirianto, F. Utaminingrum, Automatic method for optic disc segmentation using deep learning on retinal fundus images, Healthcare Informatics Research 29 (2023) 145–151

2023
[172]

A. E. Ilesanmi, T. Ilesanmi, G. A. Gbotoso, A systematic review of retinal fundus image segmentation and classification methods using convolutional neural networks, Healthcare Analytics 4 (2023) 100261

2023
[173]

K. Ren, L. Chang, M. Wan, G. Gu, Q. Chen, An improved u-net based retinal vessel image segmentation method, Heliyon 8 (2022)

2022
[174]

S. Guo, K. Wang, H. Kang, Y. Zhang, Y. Gao, T. Li, Bts-dsn: Deeply supervised neural network with short connections for retinal vessel segmentation, Int. J. Med. Inform. 126 (2019) 105–113

2019
[175]

K.Han,Y.Wang,H.Chen,X.Chen,J.Guo,Z.Liu,Y.Tang,A.Xiao,C.Xu,Y.Xu,etal., Asurveyonvisiontransformer, IEEEtransactions on pattern analysis and machine intelligence 45 (2022) 87–110

2022
[176]

Huang, J

S. Huang, J. Li, Y. Xiao, N. Shen, T. Xu, Rtnet: relation transformer network for diabetic retinopathy multi-lesion segmentation, IEEE Transactions on Medical Imaging 41 (2022) 1596–1607

2022
[177]

A. Li, M. Sun, Z. Wang, Td swin-unet: Texture-driven swin-unet with enhanced boundary-wise perception for retinal vessel segmentation, Bioengineering 11 (2024) 488

2024
[178]

N. Lv, L. Xu, Y. Chen, W. Sun, J. Tian, S. Zhang, Tcddu-net: combining transformer and convolutional dual-path decoding u-net for retinal vessel segmentation, Scientific Reports 14 (2024) 25978

2024
[179]

J. Ma, Y. He, F. Li, L. Han, C. You, B. Wang, Segment anything in medical images, Nature Communications 15 (2024) 654

2024
[180]

P. Shi, J. Qiu, S. M. D. Abaxi, H. Wei, F. P.-W. Lo, W. Yuan, Generalist vision foundation models for medical imaging: A case study of segment anything model on zero-shot medical segmentation, Diagnostics 13 (2023) 1947

2023
[181]

W. Khan, S. Leem, K. B. See, J. K. Wong, S. Zhang, R. Fang, A comprehensive survey of foundation models in medicine, IEEE Reviews in Biomedical Engineering 19 (2026) 283–304

2026
[182]

T. Wang, D. Tian, H. Zhao, J. Liu, W. Wang, C. Li, G. Liu, Hierarchical multi-scale mamba with tubular structure-aware convolution for retinal vessel segmentation, Entropy 27 (2025) 862

2025
[183]

L. Yuan, Y. Chen, T. Wang, W. Yu, Y. Shi, Z.-H. Jiang, F. E. Tay, J. Feng, S. Yan, Tokens-to-token vit: Training vision transformers from scratch on imagenet, in: Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 558–567

2021
[184]

Z.Gu,Y.Li,Z.Wang,J.Kan,J.Shu,Q.Wang, Classificationofdiabeticretinopathyseverityinfundusimagesusingthevisiontransformer and residual attention, Computational Intelligence and Neuroscience 2023 (2023) 1305583

2023
[185]

D. N. Radhakrishnan, A. P. Vinod, V. Ravindran, Eye disease identification using pyramid vision transformer, in: AIP Conference Proceedings, volume 3237, AIP Publishing LLC, 2025, p. 030018. Y. Li et al.:Preprint submitted to ElsevierPage 73 of 77

2025
[186]

Steiner, A

A. Steiner, A. Kolesnikov, X. Zhai, R. Wightman, J. Uszkoreit, L. Beyer, How to train your vit? data, augmentation, and regularization in vision transformers, arXiv preprint arXiv:2106.10270 (2021)

2021 arXiv
[187]

Y. Yang, Z. Cai, S. Qiu, P. Xu, Vision transformer with masked autoencoders for referable diabetic retinopathy classification based on large-size retina image, PLOS ONE 19 (2024) e0299265

2024
[188]

K. He, X. Chen, S. Xie, Y. Li, P. Dollár, R. Girshick, Masked autoencoders are scalable vision learners, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 16000–16009

2022
[189]

10809–10818

H.Yin,A.Vahdat,J.M.Alvarez,A.Mallya,J.Kautz,P.Molchanov, A-vit:Adaptivetokensforefficientvisiontransformer, in:Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 10809–10818

2022
[190]

Saadna, S

Y. Saadna, S. Mezzoudj, M. Khelifa, Efficient transformer architectures for diabetic retinopathy classification from fundus images: Dr- mobilevit, dr-efficientformer, and dr-swintiny, Informatica 49 (2025)

2025
[191]

D. Chen, W. Yang, L. Wang, S. Tan, J. Lin, W. Bu, Pcat-unet: Unet-like network fused convolution and transformer for retinal vessel segmentation, PLOS ONE 17 (2022) e0262689

2022
[192]

X. Hu, L. Wang, Y. Li, Ht-net: A hybrid transformer network for fundus vessel segmentation, Sensors 22 (2022) 6782

2022
[193]

Jiang, Y

M. Jiang, Y. Zhu, X. Zhang, Covi-net: A hybrid convolutional and vision transformer neural network for retinal vessel segmentation, Computers in Biology and Medicine 170 (2024) 108047

2024
[194]

K.Rezaee,F.Farnami, Innovativeapproachfordiabeticretinopathyseverityclassification:Anai-poweredtoolusingcnn-transformerfusion, Journal of Biomedical Physics & Engineering 15 (2024) 137

2024
[195]

A. Gu, T. Dao, Mamba: Linear-time sequence modeling with selective state spaces, arXiv preprint arXiv:2312.00752 (2023)

2023 arXiv
[196]

Y. Liu, G. Zhang, Y. Yang, W. Gong, X. Liu, Hcm: A hybrid cnn-mamba architecture for semi-supervised retinal vessel segmentation, in: Proceedings of the 2025 International Conference on Artificial Intelligence and Educational Systems, 2025, pp. 406–412

2025
[197]

Boulesteix,J.C.Camaradou,L.A.Celi,S.Denaxas,A.K.Denniston,B.Glocker,R.M.Golub,H.Harvey,G.Heinze,M.M.Hoffman,A.P

G.S.Collins,K.G.M.Moons,P.Dhiman,R.D.Riley,A.L.Beam,B.VanCalster,M.Ghassemi,X.Liu,J.B.Reitsma,M.vanSmeden,A.-L. Boulesteix,J.C.Camaradou,L.A.Celi,S.Denaxas,A.K.Denniston,B.Glocker,R.M.Golub,H.Harvey,G.Heinze,M.M.Hoffman,A.P. Kengne, E. Lam, N. Lee, E. W. Loder, L. Maier-Hein, ...

2024
[198]

Hernandez-Boussard, S

T. Hernandez-Boussard, S. Bozkurt, J. P. Ioannidis, N. H. Shah, Minimar (minimum information for medical ai reporting): Developing reportingstandardsforartificialintelligenceinhealthcare, JournaloftheAmericanMedicalInformaticsAssociation27(2020)2011–2015

2020
[199]

Reinke, G

A. Reinke, G. Grab, L. Maier-Hein, Challenge results are not reproducible, in: BVM Workshop, Springer, 2023, pp. 198–203

2023
[200]

Geetha, C

T. Geetha, C. Hema, Deep learning-based joint analysis of diabetic retinopathy and glaucoma in retinal fundus images, Scientific Reports 16 (2026) 3133

2026
[201]

M.D.Abràmoff,P.T.Lavin,M.Birch,N.Shah,J.C.Folk, Pivotaltrialofanautonomousai-baseddiagnosticsystemfordetectionofdiabetic retinopathy in primary care offices, NPJ digital medicine 1 (2018) 39

2018
[202]

J. Du, J. Guo, W. Zhang, S. Yang, H. Liu, H. Li, N. Wang, Ret-clip: A retinal image foundation model pre-trained with clinical diagnostic reports, in: International conference on medical image computing and computer-assisted intervention, Springer Nature Switzerland, 2024, pp. 709–719

2024
[203]

D. Shi, W. Zhang, J. Yang, S. Huang, X. Chen, P. Xu, K. Jin, S. Lin, J. Wei, M. Yusufu, et al., A multimodal visual–language foundation model for computational ophthalmology, npj Digital Medicine 8 (2025) 381

2025
[204]

Y.C.Lee,J.Cha,I.Shim,W.-Y.Park,S.W.Kang,D.H.Lim,H.-H.Won, Multimodaldeeplearningoffundusabnormalitiesandtraditional risk factors for cardiovascular risk prediction, npj Digital Medicine 6 (2023) 14

2023
[205]

K. D. K. Wardhani, S. Kasim, A. Erianda, R. Hassan, Deep learning-based method in multimodal data for diabetic retinopathy detection, International Journal on Advanced Science, Engineering & Information Technology 14 (2024)

2024
[206]

I.Hartsock,G.Rasool, Vision-languagemodelsformedicalreportgenerationandvisualquestionanswering:Areview, Frontiersinartificial intelligence 7 (2024) 1430984

2024
[207]

Da Soh, Y

Z. Da Soh, Y. Bai, K. Yu, Y. Zhou, X. Lei, S. Thakur, Z. Lee, L. C. L. Phang, Q. Peng, C. C. Xue, et al., An integrated language-vision foundation model for conversational diagnostics and triaging in primary eye care, Cell Reports Medicine 6 (2025) 102476

2025
[208]

Z. Li, D. Song, Z. Yang, D. Wang, F. Li, X. Zhang, P. E. Kinahan, Y. Qiao, Visionunite: A vision-language foundation model for ophthalmology enhanced with clinical knowledge, IEEE Transactions on Pattern Analysis and Machine Intelligence (2025)

2025
[209]

Chotcomwongse, P

P. Chotcomwongse, P. Ruamviboonsuk, A. Grzybowski, Utilizing large language models in ophthalmology: the current landscape and challenges, Ophthalmol. Ther. 13 (2024) 2543–2558

2024
[210]

Bhandari, S

A. Bhandari, S. Tyagi, A comparative evaluation of handling missing data points and modalities in electronic health records, Int. J. Med. Inform. 147 (2021) 106302

2021
[211]

X. Peng, Y. Wei, A. Deng, D. Wang, D. Hu, Balanced multimodal learning via on-the-fly gradient modulation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 8238–8247

2022
[212]

S. Kar, A. A. S. Akib, A. Hasib, S. Yaser, A. B. Azim, Temporal-enhanced interpretable multi-modal prognosis and risk stratification frameworkfordiabeticretinopathy(timm-prors), in:InternationalConferenceonComputationalIntelligenceandSoftComputing,Springer, 2025, pp. 271–284

2025
[213]

Moradi, J

M. Moradi, J. Cao-Xue, M. Eslami, M. Wang, T. Elze, N. Zebardast, Multimodal deep learning for longitudinal prediction of glaucoma progression using sequential rnfl, visual field, and clinical data, medRxiv (2025) 2025–10

2025
[214]

J.Zhang,C.Zhao,L.Zeng,H.Huang,Y.Ding,W.Chen, Tv-lstm:Multimodaldeeplearningforpredictingtheprogressionoflateage-related macular degeneration using longitudinal fundus images and genetic data, AI Sensors 1 (2025) 6

2025
[215]

E.Decenciere,G.Cazuguel,X.Zhang,G.Thibault,J.-C.Klein,F.Meyer,B.Marcotegui,G.Quellec,M.Lamard,R.Danno,etal., Teleophta: Machine learning and image processing methods for teleophthalmology, Irbm 34 (2013) 196–203. Y. Li et al.:Preprint submitted to ElsevierPage 74 of 77

2013
[216]

Kauppi, J.-K

T. Kauppi, J.-K. Kamarainen, L. Lensu, V. Kalesnykiene, I. Sorri, H. Uusitalo, H. Kälviäinen, A framework for constructing benchmark databases and protocols for retinopathy in medical image analysis, in: International Conference on Intelligent Science and Intelligent Data Engi...

2012
[217]

E. J. Carmona, M. Rincón, J. García-Feijoó, J. M. Martínez-de-la Casa, Identification of the optic nerve head with genetic algorithms, Artif Intell Med 43 (2008) 243–59

2008
[218]

Al-Diri, A

B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, S. Berry, A reference data set for retinal vessel profiles, in: 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, 2008, pp. 2262–2265

2008
[219]

Zhang, F

Z. Zhang, F. S. Yin, J. Liu, W. K. Wong, N. M. Tan, B. H. Lee, J. Cheng, T. Y. Wong, Origa-light: An online retinal fundus image database forglaucomaanalysisandresearch, in:2010AnnualinternationalconferenceoftheIEEEengineeringinmedicineandbiology,IEEE,2010, pp. 3065–3068

2010
[220]

Zhang, J

Z. Zhang, J. Liu, F. Yin, B.-H. Lee, D. W. K. Wong, K. R. Sung, Achiko-k: Database of fundus images from glaucoma patients, in: 2013 IEEE 8th conference on industrial electronics and applications (ICIEA), IEEE, 2013, pp. 228–231

2013
[221]

M. I. Meyer, A. Galdran, P. Costa, A. M. Mendonça, A. Campilho, Deep convolutional artery/vein classification of retinal vessels, in: International Conference Image Analysis and Recognition, Springer, 2018, pp. 622–630

2018
[222]

J.Sivaswamy,S.Krishnadas,G.D.Joshi,M.Jain,A.U.S.Tabish, Drishti-gs:Retinalimagedatasetforopticnervehead(onh)segmentation, in: 2014 IEEE 11th international symposium on biomedical imaging (ISBI), IEEE, 2014, pp. 53–56

2014
[223]

URL:https://www.kaggle.com/datasets/sovitrath/ diabetic-retinopathy-2015-data-colored-resized

Sovitrath, Diabetic retinopathy 2015 data colored resized, 2015. URL:https://www.kaggle.com/datasets/sovitrath/ diabetic-retinopathy-2015-data-colored-resized

2015
[224]

Pachade, P

S. Pachade, P. Porwal, D. Thulkar, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, L. Giancardo, G. Quellec, F. Mériaudeau, Retinal fundus multi-disease image dataset (rfmid): A dataset for multi-disease detection research, Data 6 (2021) 14

2021
[225]

URL:https://www.kaggle.com/datasets/sovitrath/ diabetic-retinopathy-224x224-gaussian-filtered

Sovitrath, Diabetic retinopathy 224x224 gaussian filtered, 2019. URL:https://www.kaggle.com/datasets/sovitrath/ diabetic-retinopathy-224x224-gaussian-filtered

2019
[226]

URL:https://www.kaggle.com/datasets/tanlikesmath/ diabetic-retinopathy-resized/data

tanlikesmath, Diabetic retinopathy (resized), 2019. URL:https://www.kaggle.com/datasets/tanlikesmath/ diabetic-retinopathy-resized/data

2019
[227]

URL:https://www.kaggle.com/datasets/harshitstark/ diabetic-retinopathy-diagnosis-dataset?select=DRD+Dataset

Stark, Harshit, Diabetic retinopathy diagnosis dataset, 2021. URL:https://www.kaggle.com/datasets/harshitstark/ diabetic-retinopathy-diagnosis-dataset?select=DRD+Dataset

2021
[228]

Akbar, T

S. Akbar, T. Hassan, M. U. Akram, U. U. Yasin, I. Basit, Avrdb: annotated dataset for vessel segmentation and calculation of arteriovenous ratio, in:ProceedingsoftheInternationalConferenceonImageProcessing,ComputerVision,andPatternRecognition(IPCV),TheSteering Committee of The...

2017
[229]

F. J. Fumero Batista, T. Diaz-Aleman, J. Sigut, S. Alayon, R. Arnay, D. Angel-Pereira, Rim-one dl: A unified retinal image database for assessing glaucoma using deep learning, Image Analysis & Stereology 39 (2020) 161–167

2020
[230]

1374–1378

C.Guo,M.Szemenyei,Y.Yi,Y.Xue,W.Zhou,Y.Li, Denseresidualnetworkforretinalvesselsegmentation, in:ICASSP2020-2020IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 2020, pp. 1374–1378

2020
[231]

Brandl, V

C. Brandl, V. Breinlich, K. J. Stark, S. Enzinger, M. Aßenmacher, M. Olden, F. Grassmann, J. Graw, M. Heier, A. Peters, et al., Features of age-related macular degeneration in the general adults and their dependency on age, sex, and smoking: results from the german kora study,...

2016
[232]

J. I. Orlando, H. Fu, J. B. Breda, K. Van Keer, D. R. Bathula, A. Diaz-Pinto, R. Fang, P.-A. Heng, J. Kim, J. Lee, et al., Refuge challenge: A unifiedframeworkforevaluatingautomatedmethodsforglaucomaassessmentfromfundusphotographs, MedicalImageAnalysis59(2020) 101570

2020
[233]

L. Li, M. Xu, X. Wang, L. Jiang, H. Liu, Attention based glaucoma detection: A large-scale database and cnn model, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 10571–10580

2019
[234]

M. N. Bajwa, G. A. P. Singh, W. Neumeier, M. I. Malik, A. Dengel, S. Ahmed, G1020: A benchmark retinal fundus image dataset for computer-aided glaucoma detection, in: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp. 1–7

2020
[235]

J. J. Wang, E. Rochtchina, G. Liew, A. G. Tan, T. Y. Wong, S. R. Leeder, W. Smith, A. Shankar, P. Mitchell, The long-term relation among retinal arteriolar narrowing, blood pressure, and incident severe hypertension, American Journal of Epidemiology 168 (2008) 80–88

2008
[236]

URL:https://www.kaggle.com/datasets/kushagratandon12/ diabetic-retinopathy-balanced

Tandon, Kushagra, Diabetic retinopathy balanced dataset, 2020. URL:https://www.kaggle.com/datasets/kushagratandon12/ diabetic-retinopathy-balanced

2020
[237]

K. Jin, X. Huang, J. Zhou, Y. Li, Y. Yan, Y. Sun, Q. Zhang, Y. Wang, J. Ye, Fives: A fundus image dataset for artificial intelligence based vessel segmentation, Sci Data 9 (2022) 475

2022
[238]

F. A. D. Santos, Predicting human eye diseases, 2019. URL:https://www.kaggle.com/datasets/fabianogalaxy/ dataset-with-catarats-images

2019
[239]

Venkat, Eye diseases classification, 2021

Doddi, G. Venkat, Eye diseases classification, 2021. URL:https://www.kaggle.com/datasets/gunavenkatdoddi/ eye-diseases-classification/data

2021
[240]

Ahamed, Odir5k classification, 2021

T. Ahamed, Odir5k classification, 2021. URL:https://www.kaggle.com/datasets/tanjemahamed/odir5k-classification

2021
[241]

T. K. Yoo, B. Y. Kim, H. K. Jeong, H. K. Kim, D. Yang, I. H. Ryu, Simple code implementation for deep learning-based segmentation to evaluate central serous chorioretinopathy in fundus photography, Transl Vis Sci Technol 11 (2022) 22

2022
[242]

C.P.Bragança,J.M.Torres,C.P.d.A.Soares,L.O.Macedo, Detectionofglaucomaonfundusimagesusingdeeplearningonanewimage set obtained with a smartphone and handheld ophthalmoscope, Healthcare 10 (2022) 2345

2022
[243]

URL:https://www.kaggle.com/datasets/gautamrajiitk/ retinal-fundus-image-50k

Raj, Gautam, Retinal fundus image 50k, 2024. URL:https://www.kaggle.com/datasets/gautamrajiitk/ retinal-fundus-image-50k

2024
[244]

Sabari, Fundus glaucoma detection data (pytorch format), 2021

2021
[245]

Cardozo, V

O. Cardozo, V. Ojeda, R. Parra, J. C. Mello-Román, J. L. V. Noguera, M. García-Torres, F. Divina, S. A. Grillo, C. Villalba, J. Facon, et al., Dataset of fundus images for the diagnosis of ocular toxoplasmosis, Data Brief 48 (2023) 109056. Y. Li et al.:Preprint submitted to El...

2023
[246]

A. W. Ibrahim, Retina blood vessel, 2022. URL:https://www.kaggle.com/datasets/abdallahwagih/retina-blood-vessel

2022
[247]

R. Liu, X. Wang, Q. Wu, L. Dai, X. Fang, T. Yan, J. Son, S. Tang, J. Li, Z. Gao, et al., Deepdrid: Diabetic retinopathy-grading and image quality estimation challenge, Patterns 3 (2022) 100512

2022
[248]

J. Li, L. Wang, Y. Gao, Q. Liang, L. Chen, X. Sun, H. Yang, Z. Zhao, L. Meng, S. Xue, et al., Automated detection of myopic maculopathy from color fundus photographs using deep convolutional neural networks, Eye and Vision 9 (2022) 13

2022
[249]

Gojić, Evaluation benchmark for natural robustness evaluation of retinal vessel segmentation models, 2024

G. Gojić, Evaluation benchmark for natural robustness evaluation of retinal vessel segmentation models, 2024. doi:10.5281/zenodo. 12659652

2024 doi
[250]

P. K. Darabi, Diagnosis of diabetic retinopathy, 2023. URL:https://www.kaggle.com/datasets/pkdarabi/ diagnosis-of-diabetic-retinopathy. doi:10.13140/RG.2.2.13037.19688

2023
[251]

X. Zhao, S. Chen, S. Zhang, Y. Liu, Y. Hu, D. Yuan, L. Xie, X. Luo, M. Zheng, R. Tian, et al., A fundus image dataset for intelligent retinopathy of prematurity system, Sci Data 11 (2024) 543

2024
[252]

Z. Deng, W. Gao, Z. Gong, R. Gan, L. Chen, S. Zhang, L. Ma, A fundus image dataset for ai-based artery-vein vessel segmentation, Sci Data 12 (2025) 1298

2025
[253]

Kovalyk, J

O. Kovalyk, J. Morales-Sánchez, R. Verdú-Monedero, I. Sellés-Navarro, A. Palazón-Cabanes, J.-L. Sancho-Gómez, Papila: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment, Sci Data 9 (2022) 291

2022
[254]

W. S. Lim, H.-Y. Ho, H.-C. Ho, Y.-W. Chen, C.-K. Lee, P.-J. Chen, F. Lai, J.-S. R. Jang, M.-L. Ko, Use of multimodal dataset in ai for detecting glaucoma based on fundus photographs assessed with oct: focus group study on high prevalence of myopia, BMC Med Imaging 22 (2022) 206

2022
[255]

S.Ovreiu,E.-A.Paraschiv,E.Ovreiu, Deeplearning&digitalfundusimages:Glaucomadetectionusingdensenet, in:202113thinternational conference on electronics, computers and artificial intelligence (ECAI), IEEE, 2021, pp. 1–4

2021
[256]

J.H.Kumar,C.S.Seelamantula,J.Gagan,Y.S.Kamath,N.I.Kuzhuppilly,U.Vivekanand,P.Gupta,S.Patil, Cháks .u:Aglaucomaspecific fundus image database, Scientific Data 10 (2023) 70

2023
[257]

A. S. Küren, Fundus dataset, 2023. URL:https://www.kaggle.com/datasets/ahmetselukkren/fundus-dataset

2023
[258]

Singh, R

K. Singh, R. Kapoor, Image enhancement using exposure based sub image histogram equalization, Pattern Recognition Letters 36 (2014) 10–14

2014
[259]

D. R. Brownrigg, The weighted median filter, Communications of the ACM 27 (1984) 807–818

1984
[260]

Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 8 (2009) 679–698

J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence 8 (2009) 679–698

2009
[261]

Abdushkour, T

H. Abdushkour, T. A. Soomro, A. Ali, F. Ali Jandan, H. Jelinek, F. Memon, F. Althobiani, S. Mohammed Ghonaim, M. Irfan, Enhancing fine retinal vessel segmentation: Morphological reconstruction and double thresholds filtering strategy, PLoS One 18 (2023) e0288792

2023
[262]

Xiong, H

L. Xiong, H. Li, L. Xu, An enhancement method for color retinal images based on image formation model, Comput Methods Programs Biomed 143 (2017) 137–150

2017
[263]

Nisha, G

K. Nisha, G. Sreelekha, S. P. Savithri, P. Mohanachandran, A. Vinekar, Fusion of structure adaptive filtering and mathematical morphology for vessel segmentation in fundus images of infants with retinopathy of prematurity, in: 2017 IEEE 30th Canadian Conference on Electrical a...

2017
[264]

O.Chutatape,L.Zheng,S.M.Krishnan, Retinalbloodvesseldetectionandtrackingbymatchedgaussianandkalmanfilters, in:Proceedings of the 20th annual international conference of the ieee engineering in medicine and biology society. vol. 20 biomedical engineering towards the year 2000 a...

2000
[265]

M.E.Martinez-Perez,A.D.Hughes,S.A.Thom,K.H.Parker, Improvementofaretinalbloodvesselsegmentationmethodusingtheinsight segmentationandregistrationtoolkit(itk), in:200729thAnnualInternationalConferenceoftheIEEEEngineeringinMedicineandBiology Society, IEEE, 2007, pp. 892–895

2007
[266]

Zhu, Fourier cross-sectional profile for vessel detection on retinal images, Computerized Medical Imaging and Graphics 34 (2010) 203–212

T. Zhu, Fourier cross-sectional profile for vessel detection on retinal images, Computerized Medical Imaging and Graphics 34 (2010) 203–212

2010
[267]

Z.Guo,P.Lin,G.Ji,Y.Wang, Retinalvesselsegmentationusingafiniteelementbasedbinarylevelsetmethod, InverseProblems&Imaging 8 (2014) 459

2014
[268]

Al Shehhi, P

R. Al Shehhi, P. R. Marpu, W. L. Woon, An automatic cognitive graph-based segmentation for detection of blood vessels in retinal images, Math. Probl. Eng. 2016 (2016) 7906165

2016
[269]

Odstrcilik, R

J. Odstrcilik, R. Kolar, A. Budai, J. Hornegger, J. Jan, J. Gazarek, T. Kubena, P. Cernosek, O. Svoboda, E. Angelopoulou, Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database, IET Image Processing 7 (2013) 373–383

2013
[270]

A. A. Abd El-Khalek, H. M. Balaha, N. S. Alghamdi, M. Ghazal, A. T. Khalil, M. E. A. Abo-Elsoud, A. El-Baz, A concentrated machine learning-based classification system for age-related macular degeneration (amd) diagnosis using fundus images, Sci. Rep. 14 (2024) 2434

2024
[271]

Rajinikanth, R

V. Rajinikanth, R. Sivakumar, D. J. Hemanth, S. Kadry, J. R. Mohanty, S. Arunmozhi, N. S. M. Raja, N. G. Nhu, Automated classification of retinal images into amd/non-amd class—a study using multi-threshold and gassian-filter enhanced images, Evolutionary Intelligence 14 (2021)...

2021
[272]

J.M.Ahn,S.Kim,K.-S.Ahn,S.-H.Cho,K.B.Lee,U.S.Kim, Adeeplearningmodelforthedetectionofbothadvancedandearlyglaucoma using fundus photography, PLoS One 13 (2018) e0207982

2018
[273]

Niemeijer, J

M. Niemeijer, J. Staal, B. Van Ginneken, M. Loog, M. D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, in: Medical imaging 2004: image processing, volume 5370, SPIE, 2004, pp. 648–656

2004
[274]

A.Anzalone,F.Bizzarri,M.Parodi,M.Storace, Amodularsupervisedalgorithmforvesselsegmentationinred-freeretinalimages, Comput. Biol. Med. 38 (2008) 913–922

2008
[275]

L. Xu, S. Luo, A novel method for blood vessel detection from retinal images, Biomedical Engineering Online 9 (2010) 14

2010
[276]

C. A. Lupascu, D. Tegolo, E. Trucco, Fabc: retinal vessel segmentation using adaboost, IEEE Transactions on Information Technology in Biomedicine 14 (2010) 1267–1274. Y. Li et al.:Preprint submitted to ElsevierPage 76 of 77

2010
[277]

X. You, Q. Peng, Y. Yuan, Y.-m. Cheung, J. Lei, Segmentation of retinal blood vessels using the radial projection and semi-supervised approach, Pattern Recognition 44 (2011) 2314–2324

2011
[278]

E.S.Varnousfaderani,S.Yousefi,C.Bowd,A.Belghith,M.H.Goldbaum, Vesseldelineationinretinalimagesusingleung-malikfiltersand two levels hierarchical learning, in: AMIA Annual Symposium Proceedings, volume 2015, 2015, p. 1140

2015
[279]

S.Wang,Y.Yin,G.Cao,B.Wei,Y.Zheng,G.Yang, Hierarchicalretinalbloodvesselsegmentationbasedonfeatureandensemblelearning, Neurocomputing 149 (2015) 708–717

2015
[280]

Welikala, M

R. Welikala, M. Fraz, P. Foster, P. Whincup, A. R. Rudnicka, C. G. Owen, D. P. Strachan, S. A. Barman, U. B. Eye, V. Consortium, et al., Automated retinal image quality assessment on the uk biobank dataset for epidemiological studies, Computers in biology and medicine 71 (2016) 67–76

2016
[281]

C. Zhu, B. Zou, Y. Xiang, J. Cui, H. Wu, An ensemble retinal vessel segmentation based on supervised learning in fundus images, Chinese Journal of Electronics 25 (2016) 503–511

2016
[282]

A. E. Chowdhury, G. Mann, W. H. Morgan, A. Vukmirovic, A. Mehnert, F. Sohel, Msganet-rav: A multiscale guided attention network for artery-vein segmentation and classification from optic disc and retinal images, Journal of Optometry 15 (2022) S58–S69

2022
[283]

Girard, C

F. Girard, C. Kavalec, F. Cheriet, Joint segmentation and classification of retinal arteries/veins from fundus images, Artificial Intelligence in Medicine 94 (2019) 96–109

2019
[284]

J.Morano,Á.S.Hervella,J.Novo,J.Rouco, Simultaneoussegmentationandclassificationoftheretinalarteriesandveinsfromcolorfundus images, Artificial Intelligence in Medicine 118 (2021) 102116

2021
[285]

G.Sun,X.Liu,X.Yu, Multi-pathcascadedu-netforvesselsegmentationfromfundusfluoresceinangiographysequentialimages, Computer Methods and Programs in Biomedicine 211 (2021) 106422

2021
[286]

Hemelings, B

R. Hemelings, B. Elen, I. Stalmans, K. Van Keer, P. De Boever, M. B. Blaschko, Artery–vein segmentation in fundus images using a fully convolutional network, Computerized Medical Imaging and Graphics 76 (2019) 101636

2019
[287]

Q. Jin, Z. Meng, T. D. Pham, Q. Chen, L. Wei, R. Su, Dunet: A deformable network for retinal vessel segmentation, Knowledge-Based Systems 178 (2019) 149–162

2019
[288]

T. A. Soomro, A. J. Afifi, J. Gao, O. Hellwich, L. Zheng, M. Paul, Strided fully convolutional neural network for boosting the sensitivity of retinal blood vessels segmentation, Expert Systems with Applications 134 (2019) 36–52

2019
[289]

N. T. Le, T. Le Truong, S. Deelertpaiboon, W. Srisiri, P. F. Pongsachareonnont, D. Suwajanakorn, A. Mavichak, R. Itthipanichpong, W. Asdornwised, W. Benjapolakul, et al., Vit-amd: A new deep learning model for age-related macular degeneration diagnosis from fundus images, Inte...

2024
[290]

Y.Liu,D.Yao,Y.Ma,H.Wang,J.Wang,X.Bai,G.Zeng,Y.Liu, Stmf-drnet:Amulti-branchfine-grainedclassificationmodelfordiabetic retinopathy using swin-transformerv2, Biomedical Signal Processing and Control 103 (2025) 107352

2025
[291]

R. A. Dihin, E. AlShemmary, W. Al-Jawher, Diabetic retinopathy classification using swin transformer with multi wavelet, Journal of Kufa for Mathematics and Computer 10 (2023) 167–172

2023
[292]

T. Wang, Q. Dai, Survs: A swin-unet and game theory-based unsupervised segmentation method for retinal vessel, Computers in Biology and Medicine 166 (2023) 107542

2023
[293]

J.Lin,X.Huang,H.Zhou,Y.Wang,Q.Zhang, Stimulus-guidedadaptivetransformernetworkforretinalbloodvesselsegmentationinfundus images, Medical Image Analysis 89 (2023) 102929

2023
[294]

2454–2463

M.Mehmood,M.Alsharari,S.Iqbal,I.Spence,M.Fahim,Retinalitenet:Alightweighttransformerbasedcnnforretinalfeaturesegmentation, in: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2024, pp. 2454–2463

2024
[295]

H. Xu, X. Shao, D. Fang, F. Huang, A hybrid neural network approach for classifying diabetic retinopathy subtypes, Frontiers in Medicine 10 (2024) 1293019

2024
[296]

Jiang, M

H. Jiang, M. Gao, Z. Liu, C. Tang, X. Zhang, S. Jiang, W. Yuan, J. Liu, Glanceseg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy, IEEE Journal of Biomedical and Health Informatics (2024)

2024
[297]

Y. Liu, E. Xia, C. Sun, Z. Zhou, Crma-unet: Cnn+resmamba-based and attentional mechanisms for retinal vessel segmentation, Expert Systems with Applications (2025) 129286

2025
[298]

J.Liu,Y.Zeng,J.Liang,Y.Yang,Y.Zhang,E.Cai,X.Sheng,H.Cai, Mm-unet:Morphmambau-shapedconvolutionalnetworksforretinal vessel segmentation, arXiv preprint arXiv:2511.02193 (2025)

2025
[299]

Jordan, M

J. Jordan, M. A. Lor, P. Koulen, M.-L. Shyu, S.-C. Chen, Mdf-mllm: Deep fusion through cross-modal feature alignment for contextually aware fundoscopic image classification, arXiv preprint arXiv:2509.21358 (2025). Y. Li et al.:Preprint submitted to ElsevierPage 77 of 77

2025
[2025]

doi:10.21203/rs.3.rs-8191406/v1, preprint

URL:https://doi.org/10.21203/rs.3.rs-8191406/v1. doi:10.21203/rs.3.rs-8191406/v1, preprint

doi

Pith tools

Reviewed July 31, 2026 · model on record in the stance chip above.

[1] [143]

K. B. Khan, A. A. Khaliq, A. Jalil, M. A. Iftikhar, N. Ullah, M. W. Aziz, K. Ullah, M. Shahid, A review of retinal blood vessels extraction techniques: challenges, taxonomy, and future trends, Pattern Analysis and Applications 22 (2019) 767–802

2019

[2] [1]

M.Gupta,S.Gupta,G.Palanisamy,J.S.Nisha,V.Goutham,S.ArunKumar,K.Gavaskar,G.R.Naik, Acomprehensivesurveyondetection of ocular and non-ocular diseases using color fundus images, IEEE Access 12 (2024) 194296–194321

2024

[3] [2]

Health, Visual impairment and blindness, 2015

Organization, W. Health, Visual impairment and blindness, 2015. URL:https://web.archive.org/web/20150512062236/http: //www.who.int/mediacentre/factsheets/fs282/en/

arXiv 2015

[4] [3]

Y.-C. Tham, X. Li, T. Y. Wong, H. A. Quigley, T. Aung, C.-Y. Cheng, Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis, Ophthalmology 121 (2014) 2081–2090

[5] [4]

W.L.Wong,X.Su,X.Li,C.M.G.Cheung,R.Klein,C.-Y.Cheng,T.Y.Wong, Globalprevalenceofage-relatedmaculardegenerationand disease burden projection for 2020 and 2040: a systematic review and meta-analysis, The Lancet Global Health 2 (2014) e106–e116

2020

[6] [5]

Grzybowski, K

A. Grzybowski, K. Jin, J. Zhou, X. Pan, M. Wang, J. Ye, T. Y. Wong, Retina fundus photograph-based artificial intelligence algorithms in medicine: A systematic review, Ophthalmol Ther 13 (2024) 2125–2149

2024

[7] [6]

Zhang, X

K. Zhang, X. Liu, J. Xu, J. Yuan, W. Cai, T. Chen, K. Wang, Y. Gao, S. Nie, X. Xu, et al., Deep-learning models for the detection and incidence prediction of chronic kidney disease and type 2 diabetes from retinal fundus images, Nat Biomed Eng 5 (2021) 533–545

2021

[8] [7]

Besenczi, J

R. Besenczi, J. Tóth, A. Hajdu, A review on automatic analysis techniques for color fundus photographs, Comput. Struct. Biotechnol. J. 14 (2016) 371–384

2016