Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

It is challenging to discriminate Autism spectrum disorder (ASD) from a highly
heterogeneous database, because there is a great deal of uncontrollable variability in the …

Everything is connected and thus networks are instrumental in not only modeling complex
systems with many components, but also accommodating knowledge about their …

Transformer-based methods have led to the revolutionizing of multiple computer vision
tasks. Inspired by this, we propose a transformer-based network with a channel-enhanced …

After decades of basic research with many setbacks, artificial intelligence (AI) has recently
obtained significant breakthroughs, enabling computer programs to outperform human …

Rheumatic disorders present a global health challenge, marked by inflammation and
damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate …

Image-based characterization and disease understanding involve integrative analysis of
morphological, spatial, and topological information across biological scales. The …

Objectives Closer reading of computed tomography pulmonary angiography (CTPA) scans
of patients presenting with acute pulmonary embolism (PE) may identify those at high risk of …