Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to mapping the structure and function of the brain …

The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is
incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic …

Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …

Recent years have shown great merits in utilizing neuroimaging data to understand brain
structural and functional changes, as well as its relationship to different neurodegenerative …

Inferring gene expressions from histopathological images has long been a fascinating yet
challenging task, primarily due to the substantial disparities between the two modality …

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder
that is characterized by inattention, hyperactivity, and impulsivity. The neural mechanisms …

A central challenge in training one-shot learning models is the limited representativeness of
the available shots of the data space. Particularly in the field of network neuroscience where …

Accurate and automated super-resolution image synthesis is highly desired since it has the
great potential to circumvent the need for acquiring high-cost medical scans and a time …

With the development of data science, more and more machine learning technologies have
been designed to solve complicated and challenging real-world tasks containing a large …

To explore the effectiveness of graph neural networks in the assessment of financial asset
risk, this research constructs a stock relationship network using price correlation coefficients …