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.

黑色素瘤是皮肤癌症中最严重的一种，深度神经网络在临床护理和皮肤癌症诊断方面表现出巨大潜力，但现有研究主要依赖白人肤色的数据集，忽视了多样人群肤色的诊断结果。本研究评估了有监督和自监督模型在黑人手掌、脚底和指甲等黑人肌肤部位的皮损图像中的性能，并且利用 Fitzpatrick 皮肤色表确保对黑人肌肤的性能。结果显示这些模型的普适性差，表现良好的是对白人肌肤的皮损，缺乏多样化数据集的开发是不可接受的，深度神经网络在改善诊断方面有着巨大潜力，特别是对于接触有限的人群，但包括黑人皮损是必要的，以确保这些人群能够获得包容性技术的好处。