The generative adversarial network (GAN) has advantage to fit data distribution, so it can achieve data augmentation by fitting the real distribution and synthesizing additional training data. In this way, the deep convolution model can also be well trained in the case of using a small sample medical image data set. However, some certain gaps still exist between synthetic images and real images. In order to further narrow those gaps, this paper proposed a method that applies SimGAN on cardiac magnetic resonance synthetic image optimization task. Meanwhile, the improved residual structure is used to deepen the network structure to improve the performance of the optimizer. Lastly, the experiments will show the good result of our data augmentation method based on GAN.

中文翻译：

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基于模拟生成对抗网络的心脏磁共振合成图像优化

生成对抗网络（GAN）具有适合数据分布的优势，因此它可以通过拟合实际分布并综合其他训练数据来实现数据扩充。这样，在使用小样本医学图像数据集的情况下，深度卷积模型也可以得到很好的训练。但是，合成图像和真实图像之间仍然存在某些差距。为了进一步缩小这些差距，本文提出了一种将SimGAN应用于心脏磁共振合成图像优化任务的方法。同时，改进后的残差结构用于加深网络结构，提高优化器的性能。最后，实验将证明我们基于GAN的数据增强方法取得了良好的效果。