ZCH-MN-Pediatric Dataset for Pediatric Melanocytic Nevus Research
Bibliographic record
Abstract
Pediatric MN differs significantly from adult MN, displaying distinctive morphological characteristics such as border irregularity, color heterogeneity, structural asymmetry, and specific dynamic developmental patterns. Although the majority of pediatric MN are benign, their high prevalence and potential risk for malignant transformation necessitate vigilant clinical surveillance. We introduce the ZCH-MN-Pediatric dataset, the first comprehensive, large-scale, open-access dermoscopic image repository dedicated specifically to pediatric MN research. The dataset encompasses 29,317 high-resolution dermoscopic images acquired from 4,529 pediatric patients at The Children's Hospital of Zhejiang University School of Medicine between December 2019 and January 2024. Images span seven anatomical regions, captured from multiple angles to thoroughly document lesion morphology. Alongside these images, extensive clinical metadata—including anatomical location, patient age, and gender—are meticulously recorded.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.034 |
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".