Uncertainty-Aware Domain Adaptation for Vitiligo Segmentation in Clinical Photographs

• URL: http://arxiv.org/abs/2512.11791v1
• Date: Fri, 12 Dec 2025 18:56:21 GMT
• Title: Uncertainty-Aware Domain Adaptation for Vitiligo Segmentation in Clinical Photographs
• Authors: Wentao Jiang, Vamsi Varra, Caitlin Perez-Stable, Harrison Zhu, Meredith Apicella, Nicole Nyamongo,
• Abstract summary: Accurately quantifying vitiligo extent in routine clinical photographs is crucial for longitudinal monitoring of treatment response.<n>We propose a data-efficient training strategy combining domain-adaptive pre-training on the ISIC 2019 dataset with an ROI-based dual-task loss to suppress background noise.<n>Our framework demonstrates high reliability with zero catastrophic failures and provides interpretable entropy maps to identify ambiguous regions for clinician review.
• Score: 4.19421520851419
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurately quantifying vitiligo extent in routine clinical photographs is crucial for longitudinal monitoring of treatment response. We propose a trustworthy, frequency-aware segmentation framework built on three synergistic pillars: (1) a data-efficient training strategy combining domain-adaptive pre-training on the ISIC 2019 dataset with an ROI-constrained dual-task loss to suppress background noise; (2) an architectural refinement via a ConvNeXt V2-based encoder enhanced with a novel High-Frequency Spectral Gating (HFSG) module and stem-skip connections to capture subtle textures; and (3) a clinical trust mechanism employing K-fold ensemble and Test-Time Augmentation (TTA) to generate pixel-wise uncertainty maps. Extensive validation on an expert-annotated clinical cohort demonstrates superior performance, achieving a Dice score of 85.05% and significantly reducing boundary error (95% Hausdorff Distance improved from 44.79 px to 29.95 px), consistently outperforming strong CNN (ResNet-50 and UNet++) and Transformer (MiT-B5) baselines. Notably, our framework demonstrates high reliability with zero catastrophic failures and provides interpretable entropy maps to identify ambiguous regions for clinician review. Our approach suggests that the proposed framework establishes a robust and reliable standard for automated vitiligo assessment.

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