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Updated: Dec 23, 2025
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided
Yuichi Kimura1,2, Aya Watanabe3, Takahiro Yamada4
1Graduate School of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan. ukimura@ieee.org.
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.
