Mathematical methods of image analysis for cross-sectional and longitudinal population studies.
type: Dataset
description: In medicine, large scale population analysis aim to obtain statistical information in order to understand better diseases, identify their risk factors, develop preventive and curative treatments and improve the quality of life of the patients. In this thesis, we first introduce the medical context of Alzheimer's disease, recall some concepts of statistical learning and the challenges that typically occur when applied in medical imaging. The second part focus on cross-sectional studies, i.e. at a single time point. We present an efficient method to classify white matter lesions based on support vector machines. Then we discuss the use of manifold learning techniques for image and shape analysis. Finally, we present extensions of Laplacian eigenmaps to improve the low-dimension representations of patients using the combination of imaging and clinical data. The third part focus on longitudinal studies, i.e. between several time points. We quantify the hippocampus deformations of patients via the large deformation diffeomorphic metric mapping framework to build disease progression classifiers. We introduce novel strategies and spatial regularizations for the classification and identification of biomarkers.
issued: 2026-05-06T05:53:09.416378
language: en
modified: 2026-05-06T05:53:09.416387
contact point: Organization
  fn: CCSD
keyword: alzheimers-disease
keyword: analyse-de-population
keyword: apprentissage-de-varietes
keyword: biomarker
keyword: image-processing
keyword: imagerie-medicale
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-imcomputer-science-csmedical-imaging
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: infoinfo-ticomputer-science-csimage-processing-eessiv
keyword: maladie-dalzheimer
keyword: manifold-learning
keyword: marqueur-biologique
keyword: medical-imaging
keyword: modele-predictif
keyword: population-analysis
keyword: predictive-model
keyword: regularisation
keyword: regularization
keyword: theses
keyword: traitement-dimage
landing page: https://theses.hal.science/tel-00952079

Mathematical methods of image analysis for cross-sectional and longitudinal population studies.
type: Distribution
format: HTML
issued: 2026-05-06T05:53:09.456313
modified: 2026-05-06T05:53:09.389658
access URL: https://theses.hal.science/tel-00952079

test_moissonnage_selune
type: Agent