GloW-VSNet: A scribble-based weakly supervised framework for global-view vitiligo lesion segmentation
Bibliographic record
Abstract
Vitiligo lesion identification is essential for quantifying disease severity, monitoring disease progression and assessing treatment response, particularly for objective quantification. However, segmenting vitiligo lesions from clinical images is challenging due to indistinct borders, complex backgrounds, and image artifacts. The difficulty increases when handling small and sparse lesions in global-view photographs. Fully supervised segmentation models require extensively annotated datasets, making the labelling process time-consuming and costly. To address these challenges, we propose GloW-VSNet, a scribble-guided weakly supervised segmentation method for global-view vitiligo detection. Our approach integrates differentiable feature clustering with a spatial attention mechanism based on physician-provided scribble annotations, enabling the model to focus on relevant spatial features and improve segmentation accuracy despite background noise and artifacts. Additionally, we introduce spatial continuity optimization to preserve the natural distribution of vitiligo, enhancing segmentation consistency while reducing computational demands. Extensive experiments on two public vitiligo datasets and two private datasets demonstrate that GloW-VSNet achieves state-of-the-art performance. To our knowledge, this is the first study to explore weakly supervised global-view vitiligo segmentation, addressing a critical research gap. Our method enhances the assessment of disease severity and monitoring of treatment response through an objective assessment for real-world applications. Our code is publicly available at https://github.com/YuhanZheng0327/Weakly-Supervised-Vitiligo-Lesion-Segmentation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".