论文标题
通过生成对抗网络和变异自动编码器的扩散加权磁共振大脑图像产生：比较研究
Diffusion-Weighted Magnetic Resonance Brain Images Generation with Generative Adversarial Networks and Variational Autoencoders: A Comparison Study
论文作者
论文摘要
我们表明，可以使用深层生成模型合成高质量，多样化和现实的扩散加权磁共振图像。基于专业的神经放射学家的评估和关于生成的合成脑图像质量和多样性的不同指标，我们提出了两个网络，即内省性变异自动编码器和基于样式的GAN，这些网络有资格在医学领域中保存在派遣和范围内，并访问了许多方面，并访问了许多方面。
We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. Based on professional neuroradiologists' evaluations and diverse metrics with respect to quality and diversity of the generated synthetic brain images, we present two networks, the Introspective Variational Autoencoder and the Style-Based GAN, that qualify for data augmentation in the medical field, where information is saved in a dispatched and inhomogeneous way and access to it is in many aspects restricted.
