标题： iToBoS数据集：从3D全身照片中提取的病变检测皮肤区域图像 显示英文标题

标题： The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection

摘要： 人工智能在皮肤癌诊断方面取得了显著进展，使恶性病变的快速和准确检测成为可能。 在这个领域，大多数公开可用的图像数据集由单个、孤立的皮肤病变组成，这些病变位于图像的中心。 虽然这些以病变为中心的数据集对于开发诊断算法至关重要，但它们缺乏周围皮肤的上下文，而这种上下文对于提高病变检测至关重要。 iToBoS数据集是为了应对这一挑战而创建的。 它包含100名参与者的皮肤区域的16,954张图像，使用3D全身摄影拍摄。 每张图像大致对应皮肤的$7 \times 9$ cm区域，并使用边界框对所有可疑病变进行注释。 此外，该数据集还为每张图像提供了诸如解剖位置、年龄组和日晒损伤评分等元数据。 该数据集旨在促进算法的训练和基准测试，目标是实现皮肤癌的早期检测，并将这项技术部署到非临床环境中。 显示英文摘要

摘要： Artificial intelligence has significantly advanced skin cancer diagnosis by enabling rapid and accurate detection of malignant lesions. In this domain, most publicly available image datasets consist of single, isolated skin lesions positioned at the center of the image. While these lesion-centric datasets have been fundamental for developing diagnostic algorithms, they lack the context of the surrounding skin, which is critical for improving lesion detection. The iToBoS dataset was created to address this challenge. It includes 16,954 images of skin regions from 100 participants, captured using 3D total body photography. Each image roughly corresponds to a $7 \times 9$ cm section of skin with all suspicious lesions annotated using bounding boxes. Additionally, the dataset provides metadata such as anatomical location, age group, and sun damage score for each image. This dataset aims to facilitate training and benchmarking of algorithms, with the goal of enabling early detection of skin cancer and deployment of this technology in non-clinical environments.