构建3D全身摄影皮肤图像数据集，助力皮肤癌早期检测
The iToBoS dataset: skin region images extracted from 3D total body photographs for lesion detection
- 从3D全身照片提取皮肤区域图像，保留病变周围上下文信息
- 包含16,954张图像，每张约7×9cm，标注可疑病灶边界框
- 适合研究皮肤癌智能检测算法，尤其关注非临床场景应用
人工智能显著推动了皮肤癌诊断，实现了恶性病变的快速精准检测。目前主流公开图像数据集多为单个、居中放置的孤立皮肤病变图像。尽管这类以病变为中心的数据集对诊断算法开发至关重要，但缺乏病变周围皮肤的上下文信息，而这一信息对提升检测精度极为关键。为此，我们构建了iToBoS数据集，包含100名参与者通过3D全身摄影获取的16,954张皮肤区域图像。每张图像面积约7×9厘米，所有可疑病变均用边界框标注。同时，数据集提供每张图像的解剖位置、年龄组及日晒损伤评分等元数据。该数据集旨在促进算法训练与基准测试，助力皮肤癌早期发现，并推动技术在非临床环境中的部署。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。