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hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorFiot, Jean-Baptiste*
hal.structure.identifier
dc.contributor.authorFripp, Jürgen*
hal.structure.identifierCEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
dc.contributor.authorCohen, Laurent D.
HAL ID: 738939
*
dc.date.accessioned2012-06-13T15:17:35Z
dc.date.available2012-06-13T15:17:35Z
dc.date.issued2012
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/9453
dc.language.isoenen
dc.subjectAlzheimer's diseaseen
dc.subjectclinical dataen
dc.subjectimage processingen
dc.subjectpopulation analysisen
dc.subjectManifold learningen
dc.subject.ddc006.3en
dc.titleCombining Imaging and Clinical Data in Manifold Learning: Distance-Based and Graph-Based Extensions of Laplacian Eigenmapsen
dc.typeCommunication / Conférence
dc.contributor.editoruniversityotherCSIRO Information and Commuciation Technologies (CSIRO ICT Centre) http://www3.ict.csiro.au/ CSIRO;Australie
dc.description.abstractenManifold learning techniques have been widely used to produce low-dimensional representations of patient brain magnetic resonance (MR) images. Diagnosis classifiers trained on these coordinates attempt to separate healthy, mild cognitive impairment and Alzheimer's disease patients. The performance of such classifiers can be improved by incorporating clinical data available in most large-scale clinical studies. However, the standard non-linear dimensionality reduction algorithms cannot be applied directly to imaging and clinical data. In this paper, we introduce a novel extension of Laplacian Eigenmaps that allow the computation of manifolds while combining imaging and clinical data. This method is a distance-based extension that suits better continuous clinical variables than the existing graph-based extension, which is suitable for clinical variables in finite discrete spaces. These methods were evaluated in terms of classification accuracy using 288 MR images and clinical data (ApoE genotypes, Aβ42 concentrations and mini-mental state exam (MMSE) cognitive scores) of patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) study.en
dc.identifier.citationpages4en
dc.identifier.citationpages570-573
dc.relation.ispartoftitle9th IEEE International Symposium on Biomedical Imaging (ISBI), 2012
dc.relation.ispartofpublnameIEEE
dc.relation.ispartofdate2012
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00701681en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelIntelligence artificielleen
dc.relation.ispartofisbn978-1-4577-1857-1
dc.relation.conftitleISBI 2012en
dc.relation.confdate2012-05
dc.relation.confcityBarceloneen
dc.relation.confcountryEspagneen
dc.identifier.doihttp://dx.doi.org/10.1109/ISBI.2012.6235612
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