Despite the explosive growth of neuroimaging studies aimed at analyzing the brain as a
complex system, critical methodological gaps remain to be addressed. Most tools currently …

The task of classifying X-ray data is a problem of both theoretical and clinical interest. Whilst
supervised deep learning methods rely upon huge amounts of labelled data, the critical …

Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the
outbreak of the novel COVID-19 there has been a rush for developing automatic techniques …

The relationship between structure and function is of interest in many research fields
involving the study of complex biological processes. In neuroscience in particular, the fusion …

Abstract Functional Magnetic Resonance Imaging (fMRI) is widely adopted to measure brain
activity, aiming at studying brain functions both in healthy and pathological subjects …

In this paper, stochastic collocation algorithm combined with sparse grid technique (SSCA)
is proposed to deal with the periodic steady-state analysis for nonlinear systems with …

Uncovering the complex network of the brain is of great interest to the field of neuroimaging.
Mining from these rich datasets, scientists try to unveil the fundamental biological …

We consider the problem of classifying a medical image dataset when we have a limited
amounts of labels. This is very common yet challenging setting as labelled data is …

Combining imaging modalities to synthesize their inherent strengths provides a promising
means for improving brain subnetwork identification. We propose a multimodal integration …

A major goal of imaging studies such as the (ongoing) Human Connectome Project (HCP) is
to characterize the structural network map of the human brain and identify its associations …