构建首个西非热带皮肤病多模态数据集，助力AI公平诊断
eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases
- 在科特迪瓦和加纳实地采集，覆盖47种皮肤病
- 含5623张图像、1639个病例，含病变掩码与临床描述
- 医生与AI协作标注，适合全球皮肤病研究者使用
皮肤类被忽视热带病（NTDs）在贫困热带地区造成严重健康与社会经济负担。然而，由于数据稀缺，尤其是对少数群体和罕见表现的覆盖不足，基于AI的诊断支持进展受限。现有皮肤数据集往往缺乏关键的人口统计学特征和疾病谱系信息。为此，我们推出了eSkinHealth，一个在科特迪瓦和加纳实地采集的新型皮肤病数据集。eSkinHealth包含来自1,639例患者的5,623张图像，涵盖47种皮肤疾病，聚焦西非人群中的皮肤NTDs及罕见病症。我们提出一种医生- AI协同标注范式，利用基础语言与分割模型，在皮肤科医生指导下高效生成多模态标注。除患者元数据与诊断标签外，数据集还包含语义病变掩码、实例级视觉描述和临床概念。本工作提供了一个宝贵资源与可扩展的标注框架，旨在推动更公平、准确、可解释的全球皮肤科AI工具发展。
原文摘要 · Abstract (English)
Skin Neglected Tropical Diseases (NTDs) impose severe health and socioeconomic burdens in impoverished tropical communities. Yet, advancements in AI-driven diagnostic support are hindered by data scarcity, particularly for underrepresented populations and rare manifestations of NTDs. Existing dermatological datasets often lack the demographic and disease spectrum crucial for developing reliable recognition models of NTDs. To address this, we introduce eSkinHealth, a novel dermatological dataset collected on-site in Côte d'Ivoire and Ghana. Specifically, eSkinHealth contains 5,623 images from 1,639 cases and encompasses 47 skin diseases, focusing uniquely on skin NTDs and rare conditions among West African populations. We further propose an AI-expert collaboration paradigm to implement foundation language and segmentation models for efficient generation of multimodal annotations, under dermatologists' guidance. In addition to patient metadata and diagnosis labels, eSkinHealth also includes semantic lesion masks, instance-specific visual captions, and clinical concepts. Overall, our work provides a valuable new resource and a scalable annotation framework, aiming to catalyze the development of more equitable, accurate, and interpretable AI tools for global dermatology.
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