Cross-Model Transformer Method for Medical Image Synthesis

Acquiring complementary information about tissue morphology from multimodal medical images is beneficial to clinical disease diagnosis, but it cannot be widely used due to the cost of scans. In such cases, medical image synthesis has become a popular area. Recently, generative adversarial network (GAN) models are applied to many medical image synthesis tasks and show prior performance, since they enable to capture structural details clearly. However, GAN still builds the main framework based on convolutional neural network (CNN) that exhibits a strong locality bias and spatial invariance through the use of shared weights across all positions. Therefore, the long-range dependencies have been destroyed in this processing. To address this issue, we introduce a double-scale deep learning method for cross-modal medical image synthesis. More specifically, the proposed method captures locality feature via local discriminator based on CNN and utilizes long-range dependencies to learning global feature through global discriminator based on transformer architecture. To evaluate the effectiveness of double-scale GAN, we conduct folds of experiments on the standard benchmark IXI dataset and experimental results demonstrate the effectiveness of our method.