Graph Neural Networks (GNNs) have made rapid developments in the recent years. Due to
their great ability in modeling graph-structured data, GNNs are vastly used in various …

Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Convolutional neural network (CNN) has shown dissuasive accomplishment on different
areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information …

Understanding which brain regions are related to a specific neurological disorder or
cognitive stimuli has been an important area of neuroimaging research. We propose …

Healthcare systems are under increasing strain due to a myriad of factors, from a steadily
ageing global population to the current COVID-19 pandemic. In a world where we have …

With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Deep learning has huge potential for accurate disease prediction with neuroimaging data,
but the prediction performance is often limited by training-dataset size and computing …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Mapping the connectome of the human brain using structural or functional connectivity has
become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph …