A Reference-Free Quality Enhancement Framework for Low-Quality Fundus Images
Bibliographic record
Abstract
The progression of medical image analysis methodologies has significantly assisted fundus clinical decision-making, such as disease diagnosis and lesion segmentation. However, low-quality fundus images bring a series of challenges to the automatic screening of diseases and the segmentation of lesions. Most existing methods primarily concentrate on enhancing image quality by utilizing the supervision of paired fundus images, which are difficult to collect in real medical applications. High-quality reference images are essential for guiding quality enhancement. To this end, we propose an enhancement method for low-quality fundus images, called RF-IQE, to alleviate the requirement for paired training images and only requires low-quality fundus images. Specifically, we first construct the patch-level high-/low-quality domains by employing a rule-based quality assessment scheme. Then, to achieve the fundus image quality enhancement and unified illumination styles simultaneously, we formulate them as a patch quality domain adaptation and a multi-style domain adaptation, respectively. We qualitatively and quantitatively demonstrate that our reference-free image quality enhancement network outperforms the conventional methods and exhibits comparable performance than the deep learning-based image enhancement methods with paired images on both the EyeQ and Messidor datasets. Furthermore, we also investigate the influence of the RF-IQE method on various fundus imaging analysis tasks, including vessel segmentation, optic disc segmentation, lesion segmentation, and disease classification.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".