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Alzheimer's disease early detection from sparse data using brain importance maps
2013
ELCVIA Electronic Letters on Computer Vision and Image Analysis
Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer's disease. We will present a method to extract information about the location of metabolic changes induced by Alzheimer's disease based on a machine learning approach that directly links features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to also consider the interactions between the features/voxels. We produce
doi:10.5565/rev/elcvia.531
fatcat:lmusqbmpt5adraq7ihbzkfmvcu