Early Diagnosis of Alzheimer’s Disease Using Subject-Specific Models of FDG-PET Data
Résumé
Background: In machine learning classification methods developed for dementia studies,
neuroimaging features, e.g. glucose consumption extracted from PET images, are often used to
draw the border that differentiates normality from abnormality. However, these features are
affected by the anatomical variability present in the population, which acts as a confounding factor
making the task of finding the frontier (i.e. the decision function) between normality and
abnormality very challenging.
Domaines
Imagerie médicale| Origine | Fichiers produits par l'(les) auteur(s) |
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