MSKCC Skin Tone Labeling Dataset

This dataset contains detailed skin tone annotations collected from a prospective, single-center observational study performed at Memorial Sloan Kettering Cancer Center from 2023-2024. The cohort consists of 64 adult patients who underwent full-body skin examinations by board-certified dermatologists. To ensure diverse representation across the spectrum of skin tones, patients were recruited to achieve a balanced distribution across all six Fitzpatrick Skin Types. This dataset was developed to evaluate the reliability of different skin tone labeling methods and to support fairness research in dermatologic AI. The dataset comprises both patient-level and site-level metadata for skin tone classification using the Fitzpatrick Skin Type scale, Monk Skin Tone scale, Pantone SkinTone Guide, and colorimeter readings (SkinColorCatch, Delfin Technologies). A total of 4,879 dermoscopic images are included. Skin tone assessments were collected across both lesional and non-lesional (normal skin) sites, mapped to standardized anatomic locations. All skin lesions are assumed to be benign, as they were imaged immediately following dermatologic evaluation. All data were collected under an IRB-approved protocol with informed consent. The dataset has been fully de-identified in accordance with HIPAA regulations, and no protected health information (PHI) is included.本数据集包含来自2023年至2024年间在纪念斯隆-凯特琳癌症中心（Memorial Sloan Kettering Cancer Center）开展的一项前瞻性单中心观察性研究的详细肤色标注信息。该研究队列共纳入64名成年患者，均由经委员会认证的皮肤科医师完成了全身皮肤检查。为确保肤色谱系的多样性覆盖，研究招募患者时力求所有6种菲茨帕特里克肤色分型（Fitzpatrick Skin Types）的分布保持均衡。本数据集旨在评估不同肤色标注方法的可靠性，并为皮肤人工智能领域的公平性研究提供支持。 本数据集包含患者级与位点级的元数据，用于基于菲茨帕特里克肤色分型量表、蒙克肤色量表（Monk Skin Tone scale）、潘通肤色指南（Pantone SkinTone Guide）以及由SkinColorCatch设备与德尔芬科技公司（Delfin Technologies）采集的色度计读数开展肤色分类研究。数据集共包含4879张皮肤镜图像。研究收集了皮损部位与非皮损部位（即正常皮肤）的肤色评估数据，并将其映射至标准化解剖位置。鉴于所有皮肤皮损均在皮肤科医师评估后即刻进行成像，故默认所有皮损均为良性。 所有数据均按照经伦理审查委员会（Institutional Review Board, IRB）批准的研究方案收集，并已获得受试者的知情同意。本数据集已按照《健康保险流通与责任法案》（Health Insurance Portability and Accountability Act, HIPAA）的要求完成完全去标识化处理，未包含任何受保护的健康信息（Protected Health Information, PHI）。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于皮肤色调标注和公平性AI研究的数据集，包含64名患者的4,879张皮肤镜图像，覆盖了六种Fitzpatrick皮肤类型，以确保多样性。数据集提供了多种皮肤色调标注方法（如Fitzpatrick、Monk、Pantone和颜色计读数）的元数据，旨在评估标注方法的可靠性并支持皮肤科人工智能的公平性研究。