[2603.29694] Exploring the Impact of Skin Color on Skin Lesion Segmentation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.29694 (cs) [Submitted on 31 Mar 2026] Title:Exploring the Impact of Skin Color on Skin Lesion Segmentation Authors:Kuniko Paxton, Medina Kapo, Amila Akagić, Koorosh Aslansefat, Dhavalkumar Thakker, Yiannis Papadopoulos View a PDF of the paper titled Exploring the Impact of Skin Color on Skin Lesion Segmentation, by Kuniko Paxton and 4 other authors View PDF HTML (experimental) Abstract:Skin cancer, particularly melanoma, remains a major cause of morbidity and mortality, making early detection critical. AI-driven dermatology systems often rely on skin lesion segmentation as a preprocessing step to delineate the lesion from surrounding skin and support downstream analysis. While fairness concerns regarding skin tone have been widely studied for lesion classification, the influence of skin tone on the segmentation stage remains under-quantified and is frequently assessed using coarse, discrete skin tone categories. In this work, we evaluate three strong segmentation architectures (UNet, DeepLabV3 with a ResNet50 backbone, and DINOv2) on two public dermoscopic datasets (HAM10000 and ISIC2017) and introduce a continuous pigment or contrast analysis that treats pixel-wise ITA values as distributions. Using Wasserstein distances between within-image distributions for skin-only, lesion-only, and whole-image regions, we quantify lesion skin contrast and relate it to segmentation performance across multiple metric...