Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/57874

TitleDeep learning based pipeline for fingerprinting using brain functional MRI connectivity data
Author(s)Lori, Nicolás F.
Ramalhosa, Ivo
Marques, Paulo César Gonçalves
Alves, Victor
KeywordsDeep-Learning
fMRI Fingerprinting
Data-processing Pipeline
Issue date2018
PublisherElsevier
JournalProcedia Computer Science
Abstract(s)In this work we describe an appropriate pipeline for using deep-learning as a form of improving the brain functional connectivity-based fingerprinting process which is based in functional Magnetic Resonance Imaging (fMRI) data-processing results. This pipeline approach is mostly intended for neuroscientists, biomedical engineers, and physicists that are looking for an easy form of using fMRI-based Deep-Learning in identifying people, drastic brain alterations in those same people, and/or pathologic consequences to people’s brains. Computer scientists and engineers can also gain by noticing the data-processing improvements obtained by using the here-proposed pipeline. With our best approach, we obtained an average accuracy of 0.3132 ± 0.0129 and an average validation cost of 3.1422 ± 0.0668, which clearly outperformed the published Pearson correlation approach performance with a 50 Nodes parcellation which had an accuracy of 0.237.
TypeConference paper
URIhttp://hdl.handle.net/1822/57874
DOI10.1016/j.procs.2018.10.129
ISSN1877-0509
Peer-Reviewedyes
AccessOpen access
Appears in Collections:ICVS - Artigos em Revistas Internacionais com Referee

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