Artifact-Aware Lightweight Dermoscopic Lesion Segmentation with Reproducible Corruption Robustness Evaluation

Abstract Automated dermoscopic lesion segmentation is a key step in computer-aided skin cancer analysis, yet lightweight models for mobile or point-of-care deployment can be unstable when images contain acquisition artifacts such as hair, specular highlights, skin lines, and air bubbles. This paper addresses this problem by proposing an artifact-aware lightweight U-shaped segmentation framework together with a reproducible corruption benchmark for robustness evaluation. The proposed framework first estimates artifact-prone regions through a multi-cue stem that combines Laplacian, bright-region, dark-region, and learned responses. It then uses efficient multi-scale encoder blocks with depthwise separable operations and channel gating to preserve lesion-relevant features under a limited parameter budget. During decoding, a mask-guided skip-fusion module integrates coarse lesion predictions with propagated artifact cues, reducing the direct transfer of contaminated shallow features while retaining boundary information. To evaluate robustness beyond clean ISIC validation images, we construct fixed corrupted subsets from ISIC 2017 and ISIC 2018 by applying four clinically motivated artifact categories at two severity levels, with unchanged ground-truth masks and recorded artifact metadata. Experiments compare clean-only training, direct artifact augmentation, and curriculum-based artifact exposure against representative lightweight baselines. The results show that the proposed framework maintains competitive clean-domain accuracy, improves corrupted-domain robustness, benefits from curriculum training under stronger artifact severity, and transfers better to PH2 without fine-tuning. Ablation studies further verify the complementary effects of artifact-aware input modeling, robust encoder design, and mask-guided skip fusion. Overall, this work provides a lightweight segmentation framework and a reproducible evaluation protocol for studying artifact robustness in dermoscopic image segmentation. Our code is available at:https://github.com/28001736/ARL-UNet.