Bibliographic record
Abstract
<p>Radiomics has shown considerable promise as a new, emerging<br />approach to computer-aided cancer screening. However, the idea<br />of adopting radiomics for melanoma screening has not been previously<br />explored, with clinical screening relying solely on visual assessment<br />of skin lesion, and thus suffers from low sensitivity and<br />specificity. In this study, a dermal radiomics framework is proposed<br />for computer-aided screening of melanoma, with the aim of improving<br />screening accuracy. A radiomic sequencer is designed to<br />generate radiomic sequences consisting of 367 dermal radiomic<br />features based on extracted physiological biomarkers from dermatological<br />imaging data. The extracted dermal radiomic sequences<br />were then employed to classify benign and malignant melanoma<br />via non-linear random forest classification, and showed superior<br />results in terms of sensitivity, specificity and accuracy when compared<br />to the-state-of-the-art feature models for melanoma classification.</p>
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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".