皮肤癌与深度神经网络的皮肤科医生级别分类
Andre Esteva1, Brett Kuprel1, Roberto A Novoa2,3
1Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Nature
|January 25, 2017
概括
使用深度卷积神经网络 (CNN) 的新人工智能模型可以与皮肤科医生相比较准确地分类皮肤癌. 这种在庞大的数据集上训练的人工智能工具显示出通过移动设备广泛诊断的潜力.
科学领域:
- 皮肤病学
- 人工智能
- 医学成像分析
背景情况:
- 皮肤癌的诊断主要依赖于视觉查,皮肤镜检查和组织病理学检查.
- 由于细微的视觉变化,精确地分类各种皮肤病变是具有挑战性的.
- 深度卷积神经网络 (CNN) 显示出复杂的图像分类任务的前景.
研究的目的:
- 直接从临床图像中开发和评估一个单一的CNN进行端到端的皮肤病变分类.
- 评估AI在关键皮肤癌诊断场景中的表现.
- 探索人工智能驱动的移动设备在皮肤癌查方面的潜力.
主要方法:
- 通过对2032种疾病的129,450张临床图像进行了CNN的训练.
- 使用单一的,经过端到端训练的CNN只接收像素数据和疾病标签.
- 在两种关键的二进制分类中对21位皮肤科医生进行了比较.
主要成果:
- 在分类角质细胞癌与脂质角质瘤方面,CNN与专家皮肤科医生取得了同等的成绩.
- 在区分恶性黑色素瘤和良性瘤方面,
- 这项研究证实了人工智能在分类常见和致命的皮肤癌症方面的能力.
结论:
- 一个利用CNN的人工智能系统可以在皮肤癌分类方面获得皮肤科医生级别的能力.
- 与深度神经网络集成的移动设备可以显著扩大皮肤护理的可用性.
- 这项技术为低成本,普遍的皮肤癌查提供了潜在的途径.
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