Unsupervised Segmentation and Categorization of Skin Lesions Using Adaptative Thresholds and Stochastic Features
Why this work is in the frame
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Bibliographic record
Abstract
<p>This work presents a novel unsupervised method to segment skin<br />lesions in macroscopic images, grouping the pixels into three disjoint<br />categories, namely ’skin lesion’, ’suspicious region’ and ’healthy<br />skin’. These skin region categories are obtained by analyzing the<br />agreement of adaptative thresholds applied to the different skin image<br />color channels. In the sequence we use stochastic texture features<br />to refine the suspicious regions. Our preliminary results are<br />promising, and suggest that skin lesions can be segmented successfully<br />with the proposed approach. Also, ’suspicious regions’<br />are identified correctly, where it is uncertain if they belong to skin<br />lesions or to the surrounding healthy skin.</p>
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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