Recent studies have shown that CycleGAN is a highly influential medical image synthesis model. However, the lack of sufficient constraints and the bottleneck layer in auto-encoder network usually lead to blurry image and meaningless features, which may affect medical judgment. In order to synthesize accurate and meaningful medical images, weighted feature transfer GAN (WFT-GAN) is proposed to improve the quality of generated medical image, which is applied to the synthesis of unpaired multi-modal data. WFT-GAN adopts weighted feature transfer (WFT) instead of traditional skip connection to reduce the interference of encoding information on image decoding, while retaining the advantage of skip connection to the information transmission of the generated image. Moreover, the local perceptual adversarial loss combines the VGG feature map and adversarial model to make the local features of the image more meaningful. Experiments in three data sets show that the method in this paper can synthesize higher-quality medical images.

最近的研究表明，CycleGAN是一种很有影响力的医学图像合成模型。然而，自动编码器网络中缺乏足够的约束和瓶颈层通常会导致图像模糊和无意义的特征，这可能会影响医学判断。为了合成准确有意义的医学图像，提出了加权特征转移GAN（WFT-GAN），以提高生成的医学图像的质量，并将其应用于不成对的多模态数据的合成。WFT-GAN采用加权特征转移（WFT）代替传统的跳过连接，以减少编码信息对图像解码的干扰，同时保留跳过连接对生成图像的信息传输的优势。此外，局部感知对抗性损失结合了VGG特征图和对抗模型，使图像的局部特征更有意义。在三个数据集中的实验表明，该方法可以合成更高质量的医学图像。