MST-AI:皮肤癌数据集中的皮肤颜色估计
Vahid Khalkhali1, Hayan Lee2,3,4, Joseph Nguyen2
1Electrical and Computer Engineering Department, Temple University, Philadelphia, PA 19122, USA.
Journal of imaging
|July 25, 2025
概括
这项研究介绍了MST-AI,这是一种用于在大数据集中检测皮肤色调的新方法,解决了针对不同人群的皮肤癌诊断中的AI偏差. 它可以实现更公平的AI开发,用于早期检测.
科学领域:
- 人工智能的人工智能
- 皮肤病学 皮肤病学
- 医疗成像医学成像
背景情况:
- 皮肤癌诊断的AI模型缺乏皮肤颜色数据,影响非白人个体的准确性.
- 现有的皮肤色调尺度,如菲茨帕特里克可能会引入偏见.
- 解决肤色差异对于医疗保健中的公平AI至关重要.
研究的目的:
- 提出MST-AI,一种用于在大图像数据集中检测皮肤颜色的新方法.
- 为了使皮肤癌诊断能够开发公正的AI模型.
- 为了提高AI诊断工具在不同肤色的准确性.
主要方法:
- 利用卷积神经网络进行图像处理任务,如病变移除和细分.
- 采用变异贝叶斯-高斯混合模型 (VB-GMM) 来模拟正常的皮肤色调.
- 将预测的皮肤色分布与使用Kullback-Leibler分歧 (KLD) 的蒙克皮肤色 (MST) 尺度概率分布函数 (PDF) 进行比较.
主要成果:
- MST-AI 显示出与手册注释相对验证的优越性能.
- 获得了高相关系数:肯德尔的Tau (0.68),斯皮尔曼的Rho (0.69).
- 实现了1.00的完美规范化折扣累积收益 (NDGC),表明了出色的排名准确性.
结论:
- 在大型数据集中,MST-AI有效地检测皮肤颜色,使用不那么偏见的蒙克皮肤色调 (MST) 尺度.
- 这种方法解决了像ISIC这样的数据集中的关键皮肤颜色不平衡.
- 为开发公平,准确的AI工具为所有人群的早期皮肤癌检测铺平了道路.
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