Pairing-based Ensemble Classier Learning using Convolutional Brain Multiplexes & Multi-view Brain Networks for Early Dementia Diagnosis

Anna Lisowska, Islem Rekik (Lead / Corresponding author)

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)
113 Downloads (Pure)

Abstract

The majority of works using brain connectomics for dementia diagnosis heavily relied on using structural (diffusion MRI) and functional brain connectivity (functional MRI). However, how early dementia affects the morphology of the cortical surface remains poorly understood. In this paper, we first introduce multi-view morphological brain network architecture which stacks multiple networks, each quantifying a cortical attribute (e.g., thickness). Second, to model the relationship between brain views, we propose a subject-specific convolutional brain multiplex composed of intra-layers (brain views) and inter-layers between them by convolving two consecutive views. By reordering the intra-layers,
we generate different multiplexes for each subject. Third, to distinguish demented brains from healthy ones, we propose a pairing-based ensemble classifier learning strategy, which projects each pair of brain multiplex sets onto a low-dimensional space where they are fused, then classified. Our framework achieved the best classification results for the right hemisphere 90.8% and the left hemisphere 89.5%.
Original languageEnglish
Title of host publicationConnectomics in NeuroImaging
Place of PublicationSwitzerland
PublisherSpringer
Pages42-50
Number of pages9
Volume10511
ISBN (Electronic)9783319671598
ISBN (Print)9783319671581
DOIs
Publication statusPublished - 14 Sep 2017
EventInternational Workshop on Connectomics in Neuroimaging: Held in Conjunction with MICCAI 2017 - Québec City Convention Centre, Centre des congrès de Québec, Québec , Canada
Duration: 14 Sep 2017 → …
Conference number: First International Workshop
http://munsellb.people.cofc.edu/cni.html

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10511
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Workshop on Connectomics in Neuroimaging
Abbreviated titleCNI 2017
CountryCanada
CityQuébec
Period14/09/17 → …
Internet address

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    Lisowska, A., & Rekik, I. (2017). Pairing-based Ensemble Classier Learning using Convolutional Brain Multiplexes & Multi-view Brain Networks for Early Dementia Diagnosis. In Connectomics in NeuroImaging (Vol. 10511, pp. 42-50). (Lecture Notes in Computer Science; Vol. 10511). Springer . https://doi.org/10.1007/978-3-319-67159-8_6