基于denoising扩散的前段光学连贯性断层扫描 (AS-OCT) 图像生成.
Berat Ersarı1, Muhammed Görkem Kola1, Emine Esra Karaca2
1Department of Computer Engineering, Hacettepe University, Ankara, Çankaya, Turkey.
International ophthalmology
|October 23, 2025
概括
这项研究使用Denoising Diffusion Generative Adversarial Networks (DD-GANs) 来创建合成的前段光学一致性断层扫描 (AS-OCT) 图像. 这解决了数据稀缺性和不平衡性,增强了眼科中的机器学习模型.
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:
- 前段光学一致性断层扫描 (AS-OCT) 数据集在眼科中很少.
- 数据不平衡问题阻碍了预测模型的发展.
- 需要高质量的合成数据来训练强大的机器学习模型.
研究的目的:
- 通过使用Denoising Diffusion Generative Adversarial Networks (DD-GANs) 来生成合成的AS-OCT图像.
- 创建多样化,现实的数据集,用于训练没有数据不平衡的预测模型.
- 为了解决眼科中注释AS-OCT数据的稀缺性.
主要方法:
- 在健康和不健康的AS-OCT图像上训练了两个DD-GAN模型,来自第三级转诊医院.
- 使用Fréchet初始距离 (FID) 和初始分数评估合成数据集质量.
- 在真实和合成数据上训练ResNet-50模型,以比较性能.
主要成果:
- 生成了两个合成数据集 (15.7k和100k图像).
- 获得了高质量的合成与低的FID分数 (0.17健康,0.23不健康).
- 在合成数据上训练的ResNet-50模型表现与在真实数据上训练的模型相似.
结论:
- DD-GAN有效地产生现实的和平衡的AS-OCT数据集.
- 合成数据生成解决了眼科数据的稀缺性和不平衡性,推进了医学图像分析.
- 合成医疗图像生成通过保护患者保密性来增强数据隐私.
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