4 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
Yingying Zhang, Kun Zhao, Guodong Liu +10
Alzheimer's disease (AD) progresses as a continuous biological process, whereas most existing neuroimaging-based artificial intelligence methods remain limited to discrete diagnosi…
Artemis: Anatomy-Resolved inTervention for Eliminating Multimodal NeuroImage confounderS
Siyuan Dai, Yang Du, Kun Zhao +6
Multimodal neuroimaging, integrating functional connectivity from fMRI and structural connectivity from DTI, enables non-invasive analysis of brain networks using graph neural netw…
R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression
Kun Zhao, Siyuan Dai, Yingying Zhang +9
Early detection of Alzheimer's disease (AD) requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility, yet existing mul…
Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization
Bao Hoang, Yijiang Pang, Siqi Liang +3
Independent and identically distributed (i.i.d.) data is essential to many data analysis and modeling techniques. In the medical domain, collecting data from multiple sites or inst…
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model
Haoteng Tang, Guixiang Ma, Lei Guo +3
Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional network…