Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

The airways of the lung are the primary sites of disease in asthma and cystic fibrosis. Here
we study the cellular composition and hierarchy of the mouse tracheal epithelium by single …

In this paper, we provide a framework based upon diffusion processes for finding meaningful
geometric descriptions of data sets. We show that eigenfunctions of Markov matrices can be …

In social settings, individuals interact through webs of relationships. Each individual is a
node in a complex network (or graph) of interdependencies and generates data, lots of data …

Small cell lung cancer (SCLC) is a neuroendocrine tumor treated clinically as a single
disease with poor outcomes. Distinct SCLC molecular subtypes have been defined based …

Methods of dimensionality reduction provide a way to understand and visualize the structure
of complex data sets. Traditional methods like principal component analysis and classical …

We provide a framework for structural multiscale geometric organization of graphs and
subsets of. We use diffusion semigroups to generate multiscale geometries in order to …

While the role of cortical microstructure in organising neural function is well established, it
remains unclear how structural constraints can give rise to more flexible elements of …

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of
individual-specific cortical parcellations. We have previously developed a multi-session …