DERM12345
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Skin lesion datasets provide essential information for understanding various skin conditions and developing effective diagnostic tools. They aid the artificial intelligence-based early detection of skin cancer, facilitate treatment planning, and contribute to medical education and research. Published large datasets have partially coverage the subclassifications of the skin lesions. This limitation highlights the need for more expansive and varied datasets to reduce false predictions and help improve the failure analysis for skin lesions. This study presents a diverse dataset comprising 12,345 dermatoscopic images with 40 subclasses of skin lesions, collected in Turkiye, which comprises different skin types in the transition zone between Europe and Asia. Each subgroup contains high-resolution images and expert annotations, providing a strong and reliable basis for future research. The detailed analysis of each subgroup provided in this study facilitates targeted research endeavors and enhances the depth of understanding regarding the skin lesions. This dataset distinguishes itself through a diverse structure with its 5 super classes, 15 main classes, 40 subclasses and 12,345 high-resolution dermatoscopic images. Yilmaz, A., Yasar, S.P., Gencoglan, G. et al. DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with 40 Subclasses. Sci Data 11, 1302 (2024). [https://doi.org/10.1038/s41597-024-04104-3](https://doi.org/10.1038/s41597-024-04104-3)
皮肤病变数据集为各类皮肤病症的认知与高效诊断工具的研发提供核心支撑信息。其可辅助人工智能（Artificial Intelligence）实现皮肤癌的早期筛查，助力治疗方案制定，并推动医学教育与科研事业发展。 已公开的大型数据集仅能部分覆盖皮肤病变的亚型分类体系。这一局限性凸显了对更具规模与多样性的数据集的迫切需求，以降低误诊率，优化皮肤病变诊断失误的分析流程。 本研究构建了一款多样化数据集，共收录12345张皮肤镜（dermatoscopic）图像，涵盖40类皮肤病变亚型，采集自位于欧亚过渡带、拥有多样皮肤表型的土耳其。每一类亚型均配有高分辨率图像与专家标注，为后续研究提供坚实可靠的基础。本研究针对各亚型开展的详细分析，可助力针对性研究工作，深化对皮肤病变的认知深度。该数据集凭借分层多元的结构脱颖而出：包含5个超类、15个主类、40个子类，以及12345张高分辨率皮肤镜图像。 Yilmaz A, Yasar SP, Gencoglan G, 等. DERM12345：一款包含40个亚型的大型多源皮肤镜皮肤病变数据集[J]. 科学数据, 2024, 11:1302. https://doi.org/10.1038/s41597-024-04104-3
