A skin lesion hair mask dataset with fine-grained annotations
Bibliographic record
Abstract
Occlusion of skin lesions in dermoscopic images due to hair affects the performance of computer-assisted lesion analysis algorithms. Lesion analysis can benefit from digital hair removal or realistic hair simulation techniques. To assist in that process, we have created the largest publicly available skin lesion hair segmentation mask dataset by carefully annotating 500 dermoscopic images. Compared to the existing datasets, our dataset is free of non-hair artifacts like ruler markers, bubbles, and ink marks. The dataset is also less prone to over and under segmentations because of fine-grained annotations and quality checks from multiple independent annotators. To create the dataset, first, we collected five hundred copyright-free CC0 licensed dermoscopic images covering different hair patterns. Second, we trained a deep learning hair segmentation model on a publicly available weakly annotated dataset. Third, we extracted hair masks for the selected five hundred images using the segmentation model. Finally, we manually corrected all the segmentation errors and verified the annotations by superimposing the annotated masks on top of the dermoscopic images. Multiple annotators were involved in the annotation and verification process to make the annotations as error-free as possible. The prepared dataset will be useful for benchmarking and training hair segmentation algorithms as well as creating realistic hair augmentation systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".