深度学习中耳疾病的多重分类使用合成耳图像
Yoshimaru Mizoguchi1,2, Taku Ito1, Masato Yamada2
1Department of Otorhinolaryngology, Institute of Science Tokyo, Tokyo, Japan.
Acta oto-laryngologica
|January 11, 2025
概括
当与真实图像相结合时,生成对抗网络 (GAN) 并没有提高中耳疾病的AI诊断准确性. 然而,仅在合成耳膜图像上训练的人工智能模型的准确率达到70%.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 中耳疾病的自动诊断正在与人工智能一起推进.
- 膜内镜成像是诊断的关键.
研究的目的:
- 开发用于中耳疾病的自动诊断系统.
- 利用临床 tympanic 膜图像的深度学习.
主要方法:
- 探索生成对抗网络 (GANs) 来创建合成耳图像.
- 从2016年至2021年收集了472张真实内镜图像 (正常,AOM,OME,CSOM).
- 在真实图像上训练InceptionV3模型,然后用200个StyleGAN3合成图像重新训练.
主要成果:
- 包括合成图像并没有显著提高诊断准确度,而仅仅是真实图像.
- 一个完全在合成图像上训练的模型实现了~70%的诊断准确度.
结论:
- 由GAN生成的合成图像显示了开发医疗诊断AI模型的潜力.
- 需要进一步的研究来优化合成数据集成以提高准确性.
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