Objective: An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are available for dementia with Lewy bodies and frontotemporal lobar degeneration. Therefore, this study aims to present the potential of cycle-consistent generative adversarial networks (CycleGAN) to obtain enough number of training images for AI-based computer-aided diagnosis (CAD) algorithms for diagnosing dementia.
Methods: We trained CycleGAN using 43 amyloid-negative and 45 positive images in slice-by-slice.
Results: The CycleGAN can be used to synthesize reasonable amyloid-positive images, and the continuity of slices was preserved.
Discussion: Our results show that CycleGAN has the potential to generate a sufficient number of training images for CAD of dementia.
Keywords: AI (artificial intelligence); Amyloid imaging; CAD (computer-aided diagnosis).