Pathology-Preserving Transformer Based on Multicolor Space for Low-Quality Medical Image Enhancement
Bibliographic record
Abstract
Medical images acquired under suboptimal conditions often suffer from quality degradation, such as low-light, blurring, and artifacts. Such degradations obscure the lesions and anatomical structures in medical images, making it difficult to distinguish key pathological regions. This significantly increases the risk of misdiagnosis by automated medical diagnostic systems or clinicians. To address this challenge, we propose a multi-Color space-based quality enhancement network (MSQNet) that effectively eliminates global low-quality factors while preserving pathology-related characteristics for improved clinical observation and analysis. We first revisit the properties of image quality enhancement in different color spaces, where the V-channel in the HSV space can better represent the contrast and brightness enhancement process, whereas the A/B-channel in the LAB space is more focused on the color change of low-quality images. The proposed framework harnesses the unique properties of different color spaces to optimize the image enhancement process. Specifically, we propose a pathology-preserving transformer, designed to selectively aggregate features across different color spaces and enable comprehensive multiscale feature fusion. Leveraging these capabilities, MSQNet effectively enhances low-quality RGB medical images while preserving key pathological features, thereby establishing a new paradigm in medical image enhancement. Extensive experiments on three public medical image datasets demonstrate that MSQNet outperforms traditional enhancement techniques and state-of-the-art methods, in terms of both quantitative metrics and qualitative visual assessment. MSQNet successfully improves image quality while preserving pathological features and anatomical structures, facilitating accurate diagnosis and analysis by medical professionals and automated systems.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".