Learning features for streak detection in dermoscopic color images using localized radial flux of principal intensity curvature
Bibliographic record
Abstract
Malignant melanoma (MM) is one of the most frequent types of cancers among the world's white population. Dermoscopy is a noninvasive method for early recognition of MM by which physicians assess the skin lesion according to the skin subsurface features. The presence or absence of “streaks” is one of the most important dermoscopic criteria for the diagnosis of MM. We develop a machine-learning approach for identifying streaks in dermoscopic images using a novel melanoma feature, which captures the quaternion tubularness in the color dermoscopic images, is sensitive to the radial features of streaks, and is localized to different lesion bands (e.g. the most periphery band where streaks commonly appear). We validate the classification accuracy of SVM using our novel features on 99 dermoscopic images (including images in the absence, presence of regular, and presence of irregular streaks). Compared to state-of-the-art, we obtain improved classification results by up to 9% in terms of area under ROC curves.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".