Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their
connections for the understanding of brain functions and mental disorders. Recently …

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 …

In many domains, including healthcare, biology, and climate science, time series are
irregularly sampled with varying time intervals between successive readouts and different …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

Deep learning analyses have offered sensitivity leaps in detection of cognition-related
variables from functional MRI (fMRI) measurements of brain responses. Yet, as deep models …

Accurate diagnosis of neurodevelopmental disorders is a challenging task due to the time-
consuming cognitive tests and potential human bias in clinics. To address this challenge, we …

Modeling spatiotemporal brain dynamics from high-dimensional data, such as functional
Magnetic Resonance Imaging (fMRI), is a formidable task in neuroscience. Existing …

Deep-learning models have enabled performance leaps in analysis of high-dimensional
functional MRI (fMRI) data. Yet, many previous methods are suboptimally sensitive for …