Normalization of Multimodal Brain Networks for Integral and Predictive Mapping of Neurological Disorders

Informazioni relative al progetto

NormNets

ID dell’accordo di sovvenzione: 101003403

DOI 10.3030/101003403

Progetto chiuso

Data della firma CE 20 Marzo 2020

Data di avvio 1 Aprile 2020

Data di completamento 31 Marzo 2022

Finanziato da

• SPREADING EXCELLENCE AND WIDENING PARTICIPATION

Costo totale € 145 355,52

Contributo UE € 145 355,52

Coordinato da

ISTANBUL TEKNIK UNIVERSITESI
Türkiye

Pubblicazioni

Peer reviewed articles (3)

MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations

Autori: Mustafa Burak Gurbuz and Islem Rekik
Pubblicato in: Medical Image Analysis, 2021, Pagina/e 102059, ISSN 1361-8415
Editore: Elsevier BV
DOI: 10.1016/j.media.2021.102059

Estimation of gender-specific connectional brain templates using joint multi-view cortical morphological network integration

Autori: Nada Chaari; Hatice Camgöz Akdağ; Islem Rekik; Islem Rekik
Pubblicato in: Brain Imaging and Behavior, 2021, ISSN 1931-7557
Editore: Springer Pub. Co.,
DOI: 10.1007/s11682-020-00404-5

A diagnostic unified classification model for classifying multi-sized and multi-modal brain graphs using graph alignment

Autori: AbdullahYalçin and Islem Rekik
Pubblicato in: Journal of Neuroscience Methods, 2020, ISSN 0165-0270
Editore: Elsevier BV
DOI: 10.1016/j.jneumeth.2020.109014

Conference proceedings (6)

A Federated Multigraph Integration Approach for Connectional Brain Template Learning

Autori: Hızır Can Bayram and Islem Rekik
Pubblicato in: International Workshop on Multimodal Learning for Clinical Decision Support, 2021
Editore: Springer
DOI: 10.1007/978-3-030-89847-2_4

Recurrent Multigraph Integrator Network for Predicting the Evolution of Population-Driven Brain Connectivity Templates

Autori: Oytun Demirbilek and Islem Rekik
Pubblicato in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021, Pagina/e 584--594
Editore: Springer
DOI: 10.1007/978-3-030-87234-2_55

FLAT-Net: Longitudinal Brain Graph Evolution Prediction from a Few Training Representative Templates

Autori: Guris Özen, Ahmed Nebli and Islem Rekik
Pubblicato in: International Workshop on PRedictive Intelligence In MEdicine, 2021, Pagina/e 266-278
Editore: Springer
DOI: 10.1007/978-3-030-87602-9_25

One Representative-Shot Learning Using a Population-Driven Template with Application to Brain Connectivity Classification and Evolution Prediction

Autori: Umut Guvercin, Mohammed Amine Gharsallaoui and Islem Rekik
Pubblicato in: International Workshop on PRedictive Intelligence In MEdicine, 2021, Pagina/e 25--36
Editore: Springer
DOI: 10.1007/978-3-030-87602-9_3

Foreseeing Brain Graph Evolution over Time Using Deep Adversarial Network Normalizer

Autori: Zeynep Gürler, Ahmed Nebli and Islem Rekik
Pubblicato in: International Workshop on PRedictive Intelligence In MEdicine, 2020, Pagina/e 111--122
Editore: Springer
DOI: 10.1007/978-3-030-59354-4_11

Deep graph normalizer: a geometric deep learning approach for estimating connectional brain templates

Autori: Mustafa Burak Gurbuz and Islem Rekik
Pubblicato in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020, Pagina/e 155--165
Editore: Springer
DOI: 10.1007/978-3-030-59728-3_16

Other (1)

Comparative Survey of Multigraph Integration Methods for Holistic Brain Connectivity Mapping

Autori: Nada Chaari, Hatice Camgoz Akdag and Islem Rekik
Pubblicato in: arXiv, 2022
Editore: arXiv
DOI: 10.48550/arxiv.2204.05110