Graph Convolutional Neural Networks (GCNs) are widely used for graph analysis.
Specifically, in medical applications, GCNs can be used for disease prediction on a …

Abstract Recently, Graph Convolutional Networks (GCNs) have proven to be a powerful
machine learning tool for Computer Aided Diagnosis (CADx) and disease prediction. A key …

In recent years, deep learning models have shown their advantages in neuroimage
analysis, such as brain disease diagnosis. Unfortunately, it is usually difficult to acquire …

Benefiting from the powerful expressive capability of graphs, graph-based approaches have
been popularly applied to handle multi-modal medical data and achieved impressive …

The need for computational models that can incorporate imaging data with non-imaging
data while investigating inter-subject associations arises in the task of population-based …

Federated learning has been extensively explored in privacy-preserving medical image
analysis. However, the domain shift widely existed in real-world scenarios still greatly limits …

Geometric deep learning provides a principled and versatile manner for integration of
imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks …

Exploiting the wealth of imaging and non-imaging information for disease prediction tasks
requires models capable of representing, at the same time, individual features as well as …

Graph machine learning has gained great attention in both academia and industry recently.
Most of the graph machine learning models, such as Graph Neural Networks (GNNs), are …

Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test,
demographics, etc.) data can be collected together and used for disease prediction. Such …