Cross-Model Transformer Method for Medical Image Synthesis

Figure 1

Schematic flow chart of the proposed algorithm for cross-modal medical image synthesis, which consists of generator, CNN-based local discriminator, and transformer-based global discriminator. The local discriminator guides the generator to learn structural representation with inductive bias. The global discriminator guides the generator to learn comprehensive features by utilizing long-range dependencies between patches of input image.