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

Accurate segmentation of skin lesions in dermatoscopic images is crucial for
the early diagnosis of skin cancer and improving the survival rate of patients.
However, it is still a challenging task due to the irregularity of lesion
areas, the fuzziness of boundaries, and other complex interference factors. In
this paper, a novel LCAUnet is proposed to improve the ability of complementary
representation with fusion of edge and body features, which are often paid
little attentions in traditional methods. First, two separate branches are set
for edge and body segmentation with CNNs and Transformer based architecture
respectively. Then, LCAF module is utilized to fuse feature maps of edge and
body of the same level by local cross-attention operation in encoder stage.
Furthermore, PGMF module is embedded for feature integration with prior guided
multi-scale adaption. Comprehensive experiments on public available dataset
ISIC 2017, ISIC 2018, and PH2 demonstrate that LCAUnet outperforms most
state-of-the-art methods. The ablation studies also verify the effectiveness of
the proposed fusion techniques.

本文提出了一种新型的 LCAUnet 用于改善传统方法中经常被忽视的边缘和身体特征结合的能力，通过局部交叉注意力操作在编码器阶段融合边缘和身体的特征图，进一步嵌入 PGMF 模块进行特征集成，实验证明 LCAUnet 在公共可用数据集 ISIC 2017、ISIC 2018 和 PH2 上表现优于大多数最先进的方法，该方法有利于皮肤病变的精确分割。

LCAUnet：一种增强边缘和体融合的皮肤病变分割神经网络

LCAUnet: A skin lesion segmentation network with enhanced edge and body  fusion

Training of neural networks for automated diagnosis of pigmented skin lesions
is hampered by the small size and lack of diversity of available datasets of
dermatoscopic images. We tackle this problem by releasing the HAM10000 ("Human
Against Machine with 10000 training images") dataset. We collected
dermatoscopic images from different populations acquired and stored by
different modalities. Given this diversity we had to apply different
acquisition and cleaning methods and developed semi-automatic workflows
utilizing specifically trained neural networks. The final dataset consists of
10015 dermatoscopic images which are released as a training set for academic
machine learning purposes and are publicly available through the ISIC archive.
This benchmark dataset can be used for machine learning and for comparisons
with human experts. Cases include a representative collection of all important
diagnostic categories in the realm of pigmented lesions. More than 50% of
lesions have been confirmed by pathology, while the ground truth for the rest
of the cases was either follow-up, expert consensus, or confirmation by in-vivo
confocal microscopy.

利用具有不同种族和不同获取模式的皮肤镜图像，通过不同的采集和清洗方法以及半自动工作流程和特别训练的神经网络，我们成立了 HAM10000 数据集，这个数据集由 10015 个用于学术机器学习目的的皮肤镜图像组成，覆盖养猪场多种重要诊断范畴，用于训练神经网络。