ESDiff: a joint model for low-quality retinal image enhancement and vessel segmentation using a diffusion model
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In clinical screening, accurate diagnosis of various diseases relies on the extraction of blood vessels from fundus images. However, clinical fundus images often suffer from uneven illumination, blur, and artifacts caused by equipment or environmental factors. In this paper, we propose a unified 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 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. Extensive experiments on publicly available fundus retinal datasets (i.e. DRIVE, STARE, CHASE_DB1 and EyeQ) demonstrate the effectiveness of ESDiff compared to state-of-the-art methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it