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从互联网上的非标准化临床照片中生成黑色素瘤和瘤数据集

Soo Ick Cho¹, Cristian Navarrete-Dechent², Roxana Daneshjou³

¹Lunit Inc, Seoul, Korea.

JAMA dermatology

|October 4, 2023

概括

这项研究为皮肤病学AI培训创建了大型注释和合成数据集. 在这些数据集上训练的AI模型显示了黑色素瘤和瘤检测的更好的诊断性能.

科学领域:

皮肤病学皮肤病学
人工智能的人工智能
医疗成像医学成像

背景情况:

人工智能用于诊断皮肤图像的开发受到有限的隐私保护数据的阻碍.
不标准化的互联网图像在创建强大的AI培训数据集方面存在挑战.

研究的目的:

开发使用黑色素瘤和nevus.vus的非标准化互联网图像的歧视性和生成性AI模型.
创建多样化,大规模的注释和合成数据集,用于皮肤病学AI培训.

主要方法:

使用CNN和inpainting对5619个黑色素瘤/神经瘤图像 (CAN5600) 和2006个子集 (CAN2000) 进行半自动注释.
通过使用StyleGAN2-ADA.生成132,673张预训练图像 (LESION130k) 和5000张合成图像 (GAN5000).
使用AUROC对提议数据集的EfficientNet Lite0 CNN性能进行比较分析,与使用AUROC对现有公共数据集进行比较.

主要成果:

在CAN5600,CAN2000和GAN5000上训练的EfficientNet Lite0在联合公共数据集上训练的模型相比,实现了更高或相当的平均AUROC.
在拟议的CAN5600数据集上训练的模型显示了AUROC的统计学上显著的改善 (P=.02).
增加训练数据大小,包括注释和合成图像,有利于人工智能模型的性能.

结论:

人工智能技术可以有效地从互联网来源创建注释和合成皮肤学图像数据集.
在开发的数据集 (CAN5600,LESION130k,GAN5000) 上训练的模型显示出优越或可比的诊断性能.
这些共享数据集可以推动人工智能开发,并促进皮肤病诊断医生之间的共识.

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