构建首个大规模皮肤病变分割多标注数据集，支持专家偏好与工具分析。
IMA++: ISIC Archive Multi-Annotator Dermoscopic Skin Lesion Segmentation Dataset
- 收集14,967张皮肤镜图像，2,394张含2-5个标注，共17,684个分割掩码。
- 包含标注者技能等级、工具类型等元信息，支持标注差异研究。
- 适合皮肤病影像分割、标注者建模与多专家一致性分析的研究者使用。
多标注医学图像分割是重要研究方向，但高质量标注数据成本高昂。皮肤镜成像可揭示临床照片难以观察的形态结构。然而，当前缺乏公开的、带标注者标签的大规模皮肤病变分割（SLS）数据集。本文推出ISIC MultiAnnot++，一个基于ISIC Archive的大型公共多标注皮肤病变分割数据集。最终数据集包含17,684个分割掩码，覆盖14,967张皮肤镜图像，其中2,394张图像有2至5个标注，是目前最大的公开SLS数据集。数据还包含标注者技能水平、使用的分割工具等元信息，支持标注偏好建模与标注者特征分析。我们提供了数据特性分析、数据划分方案及共识分割掩码。
原文摘要 · Abstract (English)
Multi-annotator medical image segmentation is an important research problem, but requires annotated datasets that are expensive to collect. Dermoscopic skin lesion imaging allows human experts and AI systems to observe morphological structures otherwise not discernable from regular clinical photographs. However, currently there are no large-scale publicly available multi-annotator skin lesion segmentation (SLS) datasets with annotator-labels for dermoscopic skin lesion imaging. We introduce ISIC MultiAnnot++, a large public multi-annotator skin lesion segmentation dataset for images from the ISIC Archive. The final dataset contains 17,684 segmentation masks spanning 14,967 dermoscopic images, where 2,394 dermoscopic images have 2-5 segmentations per image, making it the largest publicly available SLS dataset. Further, metadata about the segmentation, including the annotators' skill level and segmentation tool, is included, enabling research on topics such as annotator-specific preference modeling for segmentation and annotator metadata analysis. We provide an analysis on the characteristics of this dataset, curated data partitions, and consensus segmentation masks.
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。