Abstract
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.
Abstract (translated)
医学图像合成（MIS）在智能医疗领域中发挥着重要作用，大大降低了医疗诊断的经济和时间成本。然而，由于医学图像的复杂性和不同组织细胞的类似特征，现有方法在满足其生物一致性方面面临巨大挑战。为此，我们提出了混合增强生成对抗网络（HAGAN）来保持结构的真实性和组织细胞的真实性。HAGAN包括注意力混合（AttnMix）生成器、分层判别器和判别器和生成器的反向跳过连接。AttnMix一致性差分 regularization 鼓励在真实和假图像之间关注结构和组织学变异性，从而提高合成图像的病理完整性以及局部区域的特征准确性。分层判别器引入了逐像素判别反馈来增强生成器，以同时提高全局和局部细节的清晰度和鉴别度。反向跳过连接通过融合真实和合成分布特征进一步提高了准确度。我们在三个不同规模的数据集（即 COVID-CT、ACDC 和 BraTS2018）上的实验评估结果表明，HAGAN 优于现有方法，在 both high-resolution 和 low-resolution 高分辨率低分辨率方面实现了最先进的性能。
URL
https://arxiv.org/abs/2405.04902
