Hierarchical Amortized GAN for 3D High Resolution Medical Image Synthesis.

ABSTRACT: Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images, these models either cannot scale to high-resolution or are prone to patchy artifacts. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by using different configurations between training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantage

SUBMITTER: Sun L 

PROVIDER: S-EPMC9413516 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature