Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning

•Brain graph super-resolution framework for boosting neurological disorder diagnosis.•Connectional brain template estimation for graph represention in the residual space.•Multi-topology residual graph manifold learning for brain graph super-resolution.•Evaluation on large-scale synthetic and real fu...

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Vydané v:	Medical image analysis Ročník 65; s. 101768
Hlavní autori:	Mhiri, Islem, Khalifa, Anouar Ben, Mahjoub, Mohamed Ali, Rekik, Islem
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 01.10.2020 Elsevier BV
Predmet:	Autism Brain Brain connectomes Brain graph fusion Cognitive tasks Computer vision Connectional brain template learning Data analysis Embedding Glutamate receptors Graph representations Graph super-resolution Graph theory Graphical representations Graphs Image registration Learning Magnetic resonance imaging Medical diagnosis N-Methyl-D-aspartic acid receptors Neurological diseases Neurological disorder diagnosis Nodes Pattern recognition Topology Topology-based residual graph manifold learning Training Topology-based residual graph manifold learning Connectional brain template learning Graph super-resolution Brain connectomes Brain graph fusion Neurological disorder diagnosis
ISSN:	1361-8415, 1361-8423, 1361-8423
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