Automatic medical image segmentation is an unsolved problem that has captured the
attention of many researchers. The purpose of this survey is to identify a representative set of …

During image segmentation tasks in computer vision, achieving high accuracy performance
while requiring fewer computations and faster inference is a big challenge. This is especially …

The ever-growing amount of data stored in digital databases raises the question of how to
organize and extract useful knowledge. This paper outlines some current developments in …

The large amount of imaging data collected in several ongoing multi-center studies requires
automated methods to delineate brain structures of interest. We have previously reported on …

The quantitative evaluation of Artificial Intelligence (AI) systems in a clinical context is a
challenging endeavor, where the development and implementation of meaningful …

The literature manifests that schizophrenia is associated with alterations in brain network
connectivity. We investigate whether large-scale Extended Granger Causality (lsXGC) can …

Natural computing, through its repertoire of nature-inspired strategies, is playing a major role
in the development of intelligent decision-making systems. The objective is to provide …

It has been shown in the literature that marijuana use is associated with changes in brain
network connectivity. We investigate whether large-scale Extended Granger Causality …

We introduce large-scale Augmented Granger Causality (lsAGC) as a method for
connectivity analysis in complex systems. The lsAGC algorithm combines dimension …

Differentiating a solitary brain metastasis (METS) from glioblastoma multiforme (GBM) is an
important yet difficult task using current MR imaging techniques. A final diagnosis is …