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 …

Abstract In the recent 5 years (2014–2018), there has been growing interest in the use of
machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

In this paper we study the problem of content-based image retrieval. In this problem, the
most popular performance measure is the top precision measure, and the most important …

Modern e-health systems have undergone rapid development thanks to the advances in
communications, computing and machine learning technology. Especially, deep learning …

Magnetic resonance (MR) imaging is widely used in daily medical treatment. It could help in
pre-surgical, diagnosis, prognosis, and postsurgical processes. It could be beneficial for …

Graph-based transductive learning (GTL) is a powerful machine learning technique that is
used when sufficient training data is not available. In particular, conventional GTL …

Signal processing is a very useful field of study in the interpretation of signals in many
everyday applications. In the case of applications with time-varying signals, one possibility is …

Amyloid positron emission tomography (PET) imaging is a valuable tool for research and
diagnosis in Alzheimer's disease (AD). Partial volume effects caused by the limited spatial …

In this article, we explore the state-space formulation of a network process to recover from
partial observations the network topology that drives its dynamics. To do so, we employ …