A Diffusion Model-Based Joint Dual-Task Network for Low-Quality Retinal Image Enhancement and Vessel Segmentation
Bibliographic record
Abstract
In clinical screening, precise diagnosis of different diseases depends on extracting blood vessel information from fundus images. However, clinical fundus images often suffer from uneven illumination, blur, and artifacts caused by equipment or environmental factors. These problems can result in missed small blood vessels and vascular discontinuity, which hinder reliable diagnosis by optometrists or computer-aided systems. In this manuscript, we propose a joint framework, called ESDiff, to address these challenges by integrating retinal image enhancement and vessel segmentation. Specifically, we introduce a novel diffusion model-based framework for image enhancement, incorporating mask refinement as an auxiliary task via a image enhancement and vessel mask-aware diffusion model. Furthermore, we utilize low-quality retinal fundus images and their corresponding illumination maps as inputs to the modified UNet to obtain degradation factors that effectively preserve pathological features and pertinent information. This approach enhances the intermediate results within the iterative process of the diffusion model. To the best of our knowledge, this is the first utilization of a diffusion model to simultaneously achieve low-quality image enhancement and fundus vessel segmentation. Extensive experiments on publicly available fundus retinal datasets demonstrate the effectiveness of ESDiff compared to state-of-the-art methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".