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.

医学图像合成（MIS）在智能医疗领域起着重要作用，极大节省了医学诊断的经济和时间成本。我们提出了混合增强生成对抗网络（HAGAN），通过注意力混合生成器、分层鉴别器和鉴别器与生成器之间的反向跳跃连接来保持结构纹理和组织细胞的真实性，提高合成图像的病理完整性和局部特征的准确性。在 COVID-CT、ACDC 和 BraTS2018 三个不同规模的数据集上进行的实验评估表明，HAGAN 优于现有方法，在高分辨率和低分辨率方面均达到了最先进的性能。