Extracting High-Level Intuitive Features (HLIF) for Classifying Skin Lesions Using Standard Camera Images
Bibliographic record
Abstract
High-level intuitive features (HLIF) that measure asymmetry of skin lesion images obtained using standard cameras are presented. These features can be used to help dermatologists objectively diagnose lesions as cancerous (melanoma) or benign with intuitive rationale. Existing work defines large sets of low-level statistical features for analysing skin lesions. The proposed HLIFs are designed such that smaller sets of HLIFs can capture more deterministic information than large sets of low-level features. Analytical reasoning is given for each feature to show how it aptly describes asymmetry. Promising experimental results show that classification using the proposed HLIF set, although only one-tenth the size of the existing state-of-the-art low-level feature set, labels the data with better or comparable success. The best classification is obtained by combining the low-level feature set with the HLIF set.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".