Detecting specular highlights in dermatological images
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
There is an increasing interest in computer-aided diagnosis of skin cancers through analysis of dermatological images. This is processing that attempts to correlate diagnosis with skin lesion appearance (i.e., the image), by extracting visual features such as colour, pigmentation, size, etc. Presence of glare (specular highlights) can confuse such systems; highlights may obscure skin surface details and appear as additional features that are not intrinsic to the lesion. In this paper, we put forward a simple method to detect specular highlights specific to dermatological images. Knowledge of the location of specularities is advantageous since it allows us to then deal with them, either by excluding them from further processing or by attempting to recover the image data in specular regions. The proposed method is built on the dichromatic reflection model and, in a novel step, uses non-negative matrix factorization with sparseness constraints to separate the specular component.
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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