BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease.

ABSTRACT: Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn th

SUBMITTER: Cao Y 

PROVIDER: S-EPMC10326383 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature