SkinCaRe bridges the gap between what models see and how they reason. It combines
- SkinCAP — dermatologist-authored, observation-first captions
- SkinCoT — clinician-certified, hierarchical Chain-of-Thought (CoT) diagnostic narratives
Total size: 7,041 dermatologic cases.
Most dermatology datasets offer only class labels, limiting transparency and clinical utility. SkinCaRe provides both descriptive captions and structured reasoning, enabling models to describe findings and explain diagnoses.
- 4,000 images from Fitzpatrick 17k and DDI
- Captions cover: anatomic site, primary/secondary morphology, color, distribution, surface changes
- Format
- Images:
.png - Metadata:
.csvwithid,disease,caption_zh,caption_en, source links
- Images:
- 3,041 image–text pairs, clinician-reviewed on six axes: Accuracy, Safety, Medical Groundedness, Clinical Coverage, Reasoning Coherence, Description Precision
- Structure
- Images by category:
SkinCoT/images/<disease_class>/<image>.jpg - CoT (English):
SkinCoT/EN/<disease_class>/<image>.jpg.txt - CoT (Chinese):
SkinCoT/ZH/<disease_class>/<image>.jpg.txt
Examples
- Image:
SkinCoT/images/Urticaria Hives/dermagraphism-32.jpg
CoT-EN:SkinCoT/EN/Urticaria Hives/dermagraphism-32.jpg.txt
CoT-ZH:SkinCoT/ZH/Urticaria Hives/dermagraphism-32.jpg.txt
- Repository: https://huggingface.co/datasets/yuhos16/SkinCaRe
- License: CC-BY-4.0
- Modalities: Image + Text (PNG, JPEG, CSV, TXT)
- Updates to images/metadata will be noted on the dataset page.
- Train/evaluate dermatology VLMs for captioning + reasoning
- Research on medical explainability and trustworthy AI
- If you find SkinCaRe helpful for your research, please consider citing:
@misc{shen2025skincaremultimodaldermatologydataset,
title={SkinCaRe: A Multimodal Dermatology Dataset Annotated with Medical Caption and Chain-of-Thought Reasoning},
author={Yuhao Shen and Liyuan Sun and Yan Xu and Wenbin Liu and Shuping Zhang and Shawn Afvari and Zhongyi Han and Jiaoyan Song and Yongzhi Ji and Tao Lu and Xiaonan He and Xin Gao and Juexiao Zhou},
year={2025},
eprint={2405.18004},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2405.18004},
}