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使用生成对抗网络进行数据增强的合成形图像生成

Maria Waqas¹, Shehzad Hasan¹, Ammar Farid Ghori¹

¹Department of Computer and Information Systems Engineering, NED University of Engineering and Technology, Karachi, Pakistan.

International dental journal

|September 2, 2025

概括

这项研究引入了一种使用生成对抗网络 (GAN) 创建现实的合成牙X射线图像的新方法,以解决人工智能开发的数据短缺问题.

科学领域:

医学成像
人工智能
计算机视觉

背景情况:

有注释的牙科X射线数据集很少,阻碍了AI的发展.
现有的数据集带来了隐私和资源的限制.

研究的目的:

开发一种用于生成高分辨率合成骨干图像 (OPG) 的新型管道.
解决AI应用有限的注释牙科X射线数据的挑战.

主要方法:

使用定制生成对抗网络 (GAN) 训练4777个真实的OPG图像.
在2048 × 1024分辨率下生成合成OPG,保留精细的解剖细节.
使用YOLO物体检测模型和专家牙医评分评估合成图像.

主要成果:

选择的GAN模型产生了具有准确结构表示的真实合成OPG.
YOLO探测器在合成图像上表现出强大的性能,表明可靠的特征表示.
专家评估证实了高解剖学可信度,顶级模型达到实际OPG分数的50%以上.

结论:

基于GAN的管道能够以道德和可扩展的方式创建合成OPG图像.
这种方法有效地扩大了人工智能驱动的牙科诊断数据集.
在数据有限或隐私敏感的环境中提供人工智能模型开发的实用解决方案.

关键词:

数据增强和人工智能牙科成像生成对抗网络 (GAN)整形眼镜 (OPG) 射线

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