论文标题
以患者为中心的图像和元数据数据集，用于使用临床环境鉴定黑色素瘤
A Patient-Centric Dataset of Images and Metadata for Identifying Melanomas Using Clinical Context
论文作者
论文摘要
先前的皮肤图像数据集尚未解决从同一患者的多个皮肤病变中获得的患者级信息。尽管人工智能分类算法在检查单一图像的对照研究中已经实现了专家级别的表现，但实际上，皮肤科医生从同一患者的多个病变中以整体判断为基础。本文所述的2020年SIIM-ISIC分类挑战数据集是为了解决先前挑战和临床实践之间的这种差异，为数据集中的每个图像提供了一个标识符，允许同一患者的病变彼此映射。临床医生经常使用此患者级别的上下文信息来诊断黑色素瘤，并且在排除许多非典型NEVI患者的假阳性方面特别有用。该数据集代表来自三个大洲的2,056例患者，每位患者平均16个病变，由33,126个皮肤镜图像和584例组织病理学证实的黑色素瘤组成，与良性黑色素瘤相比。
Prior skin image datasets have not addressed patient-level information obtained from multiple skin lesions from the same patient. Though artificial intelligence classification algorithms have achieved expert-level performance in controlled studies examining single images, in practice dermatologists base their judgment holistically from multiple lesions on the same patient. The 2020 SIIM-ISIC Melanoma Classification challenge dataset described herein was constructed to address this discrepancy between prior challenges and clinical practice, providing for each image in the dataset an identifier allowing lesions from the same patient to be mapped to one another. This patient-level contextual information is frequently used by clinicians to diagnose melanoma and is especially useful in ruling out false positives in patients with many atypical nevi. The dataset represents 2,056 patients from three continents with an average of 16 lesions per patient, consisting of 33,126 dermoscopic images and 584 histopathologically confirmed melanomas compared with benign melanoma mimickers.
