6 citations · 26 across the 11 of their papers we have counts for
5 papers · 1 filter
BDHT: Generative AI Enables Causality Analysis for Mild Cognitive Impairment
Qiankun Zuo, Ling Chen, Yanyan Shen +3
Effective connectivity estimation plays a crucial role in understanding the interactions and information flow between different brain regions. However, the functional time series u…
Alzheimer's Disease Prediction via Brain Structural-Functional Deep Fusing Network
Qiankun Zuo, Junren Pan, Shuqiang Wang
Fusing structural-functional images of the brain has shown great potential to analyze the deterioration of Alzheimer's disease (AD). However, it is a big challenge to effectively f…
Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI
Qiankun Zuo, Yanfei Zhu, Libin Lu +3
Brain network analysis is a useful approach to studying human brain disorders because it can distinguish patients from healthy people by detecting abnormal connections. Due to the…
Multiscale Autoencoder with Structural-Functional Attention Network for Alzheimer's Disease Prediction
Yongcheng Zong, Changhong Jing, Qiankun Zuo
The application of machine learning algorithms to the diagnosis and analysis of Alzheimer's disease (AD) from multimodal neuroimaging data is a current research hotspot. It remains…
Cross-Modal Transformer GAN: A Brain Structure-Function Deep Fusing Framework for Alzheimer's Disease
Junren Pan, Shuqiang Wang
Cross-modal fusion of different types of neuroimaging data has shown great promise for predicting the progression of Alzheimer's Disease(AD). However, most existing methods applied…