SkinCAP: A Multi-modal Dermatology Dataset Annotated with Rich Medical Captions
<|reference_start|>SkinCAP: A Multi-modal Dermatology Dataset Annotated with Rich Medical Captions: With the widespread application of artificial intelligence (AI), particularly deep learning (DL) and vision-based large language models (VLLMs), in skin disease diagnosis, the need for interpretability becomes crucial. However, existing dermatology datasets are limited in their inclusion of concept-level meta-labels, and none offer rich medical descriptions in natural language. This deficiency impedes the advancement of LLM-based methods in dermatological diagnosis. To address this gap and provide a meticulously annotated dermatology dataset with comprehensive natural language descriptions, we introduce SkinCAP: a multi-modal dermatology dataset annotated with rich medical captions. SkinCAP comprises 4,000 images sourced from the Fitzpatrick 17k skin disease dataset and the Diverse Dermatology Images dataset, annotated by board-certified dermatologists to provide extensive medical descriptions and captions. Notably, SkinCAP represents the world's first such dataset and is publicly available at https://huggingface.co/datasets/joshuachou/SkinCAP.<|reference_end|>
@article{zhou2024skincap:,
    title={SkinCAP: A Multi-modal Dermatology Dataset Annotated with Rich Medical
  Captions},
    author={Juexiao Zhou, Liyuan Sun, Yan Xu, Wenbin Liu, Shawn Afvari, Zhongyi
  Han, Jiaoyan Song, Yongzhi Ji, Xiaonan He, Xin Gao},
    journal={arXiv preprint arXiv:2405.18004},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18004},
    primaryClass={cs.CV}
}