High-resolution conditional MR image synthesis through the PACGAN framework.

ABSTRACT: Deep learning algorithms trained on medical images often encounter limited data availability, leading to overfitting and imbalanced datasets. Synthetic datasets can address these challenges by providing a priori control over dataset size and balance. In this study, we present PACGAN (Progressive Auxiliary Classifier Generative Adversarial Network), a proof-of-concept framework that effectively combines Progressive Growing GAN and Auxiliary Classifier GAN (ACGAN) to generate high-quality, class-specific synthetic medical images. PACGAN leverages latent space information to perform conditional synthesis of high-resolution brain magnetic resonance (MR) images, specifically targeting Alzheimer's disease patients and healthy controls. Trained on the Alzheimer's Disease Neuroimaging Initiative (

SUBMITTER: Lai M 

PROVIDER: S-EPMC12489083 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature