eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909752869519360 |
|---|---|
| author | Wang, Janet Hu, Xin Zhang, Yunbei Almamy, Diabate Bamba, Vagamon Koffi, Konan Amos Sébastien Aubin, Yao Koffi Ding, Zhengming Hamm, Jihun Yotsu, Rie R. |
| author_facet | Wang, Janet Hu, Xin Zhang, Yunbei Almamy, Diabate Bamba, Vagamon Koffi, Konan Amos Sébastien Aubin, Yao Koffi Ding, Zhengming Hamm, Jihun Yotsu, Rie R. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18608 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases Wang, Janet Hu, Xin Zhang, Yunbei Almamy, Diabate Bamba, Vagamon Koffi, Konan Amos Sébastien Aubin, Yao Koffi Ding, Zhengming Hamm, Jihun Yotsu, Rie R. Artificial Intelligence 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. |
| title | eSkinHealth: A Multimodal Dataset for Neglected Tropical Skin Diseases |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2508.18608 |