RPCA with Log-Schatten Norm and Adaptive Histogram Equalization for Medical Imaging
Bibliographic record
Abstract
Medical imaging, especially cancer and retinal fundus analysis, is often compromised by artifacts and heavy noise and artifact, which can hinder accurate diagnosis. Existing low-rank sparse component methods, such as RPCA with the conventional nuclear norm, assume uniform singular value weights, which may not hold true due to noise variations in images. We recently developed RPCA with the log-weighted nuclear norm, which addresses some of these issues but still relies on weight selection, potentially introducing bias. To overcome these limitations, we propose a novel method that integrates RPCA with Log-Schatten Norm (LSN) and Adaptive Histogram Equalization (AHE) for medical imaging and clinical purposes. The Log-Schatten Norm improves singular value penalization and structure preservation, while AHE enhances contrast and reduces noise. The method is formulated as an optimization problem and solved using the Alternating Direction Method for Multipliers (ADMM). Experimental results on publicly available retinal and cancer image datasets demonstrate that our method outperforms existing methods in enhancing overall image quality, making it a promising tool for medical imaging applications.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".