Melanoma is the most severe type of skin cancer due to its ability to cause metastasis. It is more common in black people, often affecting acral regions: palms, soles, and nails. Deep neural networks have shown tremendous potential for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing studies predominantly rely on datasets of white skin tones, neglecting to report diagnostic outcomes for diverse patient skin tones. In this work, we evaluate supervised and self-supervised models in skin lesion images extracted from acral regions commonly observed in black individuals. Also, we carefully curate a dataset containing skin lesions in acral regions and assess the datasets concerning the Fitzpatrick scale to verify performance on black skin. Our results expose the poor generalizability of these models, revealing their favorable performance for lesions on white skin. Neglecting to create diverse datasets, which necessitates the development of specialized models, is unacceptable. Deep neural networks have great potential to improve diagnosis, particularly for populations with limited access to dermatology. However, including black skin lesions is necessary to ensure these populations can access the benefits of inclusive technology.

翻译：恶性黑色素瘤因其易转移的特性，是最严重的皮肤癌类型。该病在黑人群体中更为常见，常累及肢端部位：手掌、足底及甲床。深度神经网络在改善临床护理和皮肤癌诊断方面展现出巨大潜力。然而，现有研究主要依赖浅肤色人群数据集，未报告不同肤色患者的诊断结果。本研究针对黑人群体常见的肢端区域皮肤病变图像，评估监督式与自监督式模型。同时，我们精心整理了包含肢端皮肤病变的数据集，并依据菲茨帕特里克皮肤分型量表（Fitzpatrick scale）对数据集进行评估以验证其对黑色皮肤的检测性能。结果表明，这些模型的泛化能力欠佳，其对浅色皮肤病变的表现更为优异。忽视构建多样性数据集——这需要开发专门的模型——是不可接受的。深度神经网络在改善诊断方面潜力巨大，尤其对获得皮肤病诊疗服务有限的群体而言。但若要确保这些群体能受益于包容性技术，必须纳入黑色皮肤病变数据。