条件 GAN (pix2pix) 还是 CycleGAN？
我有一个数据集，包括配对的MRI和患者CT。我的目的是从MRI图像产生合成CT。当我配对图像时，哪个GAN网络最适合此目的？ Cyclegan还是Pix2Pix？哪一个导致质量更高的合成CT？我可以使用Cyclegan以未配对的方式用配对图像为模型喂食吗？出于我的目的，Cyclegan比Pix2Pix有任何优势吗？
任何建议都将不胜感激。
I have a dataset including paired MRI and CT of patients. My aim is generating synthetic CT from MRI images. As I have paired images, which GAN network is the best for this purpose? CycleGAN OR pix2pix? Which one result in a synthetic CT with a higher quality? Can I use CycleGAN to feed the model with paired images in an unpaired manner? Does CycleGAN has any advantages over the pix2pix for my purpose?
Any advice would be highly appreciated ????
如果你对这篇内容有疑问，欢迎到本站社区发帖提问 参与讨论，获取更多帮助，或者扫码二维码加入 Web 技术交流群。
绑定邮箱获取回复消息
由于您还没有绑定你的真实邮箱，如果其他用户或者作者回复了您的评论，将不能在第一时间通知您！
发布评论
评论（1）
我们俩都不知道。但是，您可以将有条件的自行车加成以控制配对的图像。就我而言，数据集通过减少不良样品的数量来决定图像的质量。 Pix2Pix和Cyclegan都可以很好地工作。如果您专注于更高的分辨率（更敏捷但嘈杂），则可以选择Resnet作为生成器。如果您的任务是细分的，我认为u-net最好使用（ https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-019-019-0682-x ）
We both don't know that. But you can make conditional CycleGAN to control paired images. In my case, the dataset decided the quality of image by reduce the number of bad samples. Both pix2pix and CycleGAN can work well. If you focused on higher resolution (sharper but noisier), you can choose ResNet as Generator. If your task was segmentation, I think U-Net is better to use (https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-019-0682-x)