视网膜图像翻译的生成人工智能,以提高通过深度学习来改善玻璃眼查.
Iyad Majid1, Nazlee Zebardast2, Mengyu Wang1
1Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
Translational vision science & technology
|November 21, 2025
概括
生成对抗性网络 (GAN) 从SLO数据创建合成色底图像,显著改进了自动化玻璃眼瘤检测模型和数据集一致性.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 自动化青光眼检测依赖于高质量的 fundus 图像.
- 扫描激光眼镜镜 (SLO) 图像在青光眼的分类中未得到充分利用.
- 在SLO和色底图像 (CF) 之间的域移动阻碍了模型性能.
研究的目的:
- 通过使用生成对抗网络 (GANs) 来增强自动眼病检测.
- 将未充分利用的SLO fundus图像翻译成合成CF照片.
- 改进用于格洛科马分类的深度学习模型的性能.
主要方法:
- 使用循环一致的GAN (CycleGAN) 框架,从16936张SLO图像中生成合成CF图像.
- 通过使用真实CF,合成CF,SLO和组合数据集训练了五个深度学习模型.
- 模型性能是使用运行特征曲线下的面积 (AUC) 和在90%和95%的特异性时的灵敏度来评估的.
主要成果:
- 在真实和合成CF图像上训练的"GAN+CFP"模型实现了最高的AUC (0.94) 和卓越的灵敏度 (0.83在90%特异性,0.77在0.77).
- 该模型的性能优于仅在真实CF (AUC=0.89) 或SLO图像 (AUC=0.82) 上训练的模型.
- "GAN+CFP"模型在各种种族和种族群体中表现一致.
结论:
- GAN有效地将SLO图像转换为合成CF照片,解决域移位问题.
- 这种翻译增加了数据集的大小,并提高了培训数据的一致性.
- GANs提供了一种可行的方法来增强自动眼瘤检测模型.
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