Automatic detection and inpainting of specular reflections for colposcopic images
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Specular reflections are not wanted in images because they can really reduce the performance of image processing techniques. This is particularly true for medical images and especially for colposcopic images. There are several methods in the literature allowing to extract specular reflections, but only a few methods can perform an automatic extraction. In this paper, we propose a new method to extract and to restore specularities automatically. This method is based on Dichromatic Reflection Model (DRM) and multi-resolution inpainting technique (MIT). The DRM approach will retrieve specularities while the MIT technique re-establish colors in bright zones using local information. The proposed method achieves good results and does not need any a priori knowledge. The efficiency of this method for colposcopic images has been demonstrated through a collaboration with the oncology center of Marrakech University Hospital.
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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 | 0.000 | 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