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 38 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 photos 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 5 super
classes, 15 main classes, 38 subclasses and its 12,345 high-resolution
dermatoscopic images.

这篇研究提供了一个多样化的数据集，包括 12,345 个高分辨率的皮肤镜图像，涵盖了 38 个皮肤病变的亚类，为皮肤病变的早期诊断提供了可靠的基础和有针对性的研究。

DERM12345：一个包含 38 个子类别的大型多源皮肤镜病变数据集

DERM12345: A Large, Multisource Dermatoscopic Skin Lesion Dataset with  38 Subclasses

This study explores the utilization of Dermatoscopic synthetic data generated
through stable diffusion models as a strategy for enhancing the robustness of
machine learning model training. Synthetic data generation plays a pivotal role
in mitigating challenges associated with limited labeled datasets, thereby
facilitating more effective model training. In this context, we aim to
incorporate enhanced data transformation techniques by extending the recent
success of few-shot learning and a small amount of data representation in
text-to-image latent diffusion models. The optimally tuned model is further
used for rendering high-quality skin lesion synthetic data with diverse and
realistic characteristics, providing a valuable supplement and diversity to the
existing training data. We investigate the impact of incorporating newly
generated synthetic data into the training pipeline of state-of-art machine
learning models, assessing its effectiveness in enhancing model performance and
generalization to unseen real-world data. Our experimental results demonstrate
the efficacy of the synthetic data generated through stable diffusion models
helps in improving the robustness and adaptability of end-to-end CNN and vision
transformer models on two different real-world skin lesion datasets.

研究探讨了通过稳定扩散模型生成的皮肤镜合成数据的利用，作为增强机器学习模型训练鲁棒性的策略。实验结果表明，通过稳定扩散模型生成的合成数据有助于提高卷积神经网络和视觉转换器模型的鲁棒性和适应性，对于两个不同的真实世界皮肤病变数据集，其效果显著。