Training Melanoma Detection in Photographs Using the Perceptual Expertise Training Approach
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
Summary Although a deadly form of skin cancer, melanoma is treatable if detected early. However, current rule‐based training practices in melanoma detection are not effective. We assessed an innovative technique to train melanoma detection using the perceptual expertise principles. Participants in the training group were trained to categorize melanoma and benign lesions to 95% accuracy. Participants in the control group received no training. Prior to testing all participants reviewed the ABCDE rules. Training was evaluated by the pre and post tests using the Melanoma Detection Test where participants categorized images of melanoma and benign lesions. As compared to the control group, the training group showed significant improvement in melanoma detection and became less liberal (i.e., bias toward categorizing a lesion as melanoma), and both improvements maintained a week after the training. These findings indicate that perceptual expertise training is a promising approach to train melanoma detection.Copyright © 2016 John Wiley & Sons, Ltd.
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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