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dc.contributor.authorGalazzo, IBen_US
dc.contributor.authorCruciani, Fen_US
dc.contributor.authorBrusini, Len_US
dc.contributor.authorSalih, Aen_US
dc.contributor.authorRadeva, Pen_US
dc.contributor.authorStorti, SFen_US
dc.contributor.authorMenegaz, Gen_US
dc.date.accessioned2022-11-17T11:50:30Z
dc.date.issued2022en_US
dc.identifier.issn1053-5888en_US
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/82549
dc.format.extent99 - 116en_US
dc.relation.ispartofIEEE SIGNAL PROCESSING MAGAZINEen_US
dc.subjectNeuroimagingen_US
dc.subjectDeep learningen_US
dc.subjectMagnetic resonance imagingen_US
dc.subjectAgingen_US
dc.subjectPredictive modelsen_US
dc.titleExplainable Artificial Intelligence for Magnetic Resonance Imaging Aging Brainprints: Grounds and challengesen_US
dc.typeArticle
dc.identifier.doi10.1109/MSP.2021.3126573en_US
pubs.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000761217500017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=612ae0d773dcbdba3046f6df545e9f6aen_US
pubs.issue2en_US
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.volume39en_US


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