  Medical Image Synthesis (MIS) plays an important role in the intelligent
medical field, which greatly saves the economic and time costs of medical
diagnosis. However, due to the complexity of medical images and similar
characteristics of different tissue cells, existing methods face great
challenges in meeting their biological consistency. To this end, we propose the
Hybrid Augmented Generative Adversarial Network (HAGAN) to maintain the
authenticity of structural texture and tissue cells. HAGAN contains Attention
Mixed (AttnMix) Generator, Hierarchical Discriminator and Reverse Skip
Connection between Discriminator and Generator. The AttnMix consistency
differentiable regularization encourages the perception in structural and
textural variations between real and fake images, which improves the
pathological integrity of synthetic images and the accuracy of features in
local areas. The Hierarchical Discriminator introduces pixel-by-pixel
discriminant feedback to generator for enhancing the saliency and discriminance
of global and local details simultaneously. The Reverse Skip Connection further
improves the accuracy for fine details by fusing real and synthetic
distribution features. Our experimental evaluations on three datasets of
different scales, i.e., COVID-CT, ACDC and BraTS2018, demonstrate that HAGAN
outperforms the existing methods and achieves state-of-the-art performance in
both high-resolution and low-resolution.
