Alzheimer's disease early detection from sparse data using brain importance maps

Andreas Kodewitz, Sylvie Lelandais, Christophe Montagne, Vincent Vigneron


Statistical methods are increasingly used in the analysis of FDG-PET images for the early diagnosis of Alzheimer’s disease. We will demonstrate a method to extract information about the location of metabolic changes induced by Alzheimer’s disease based on a machine learning approach that directly relies features and brain areas to search for regions of interest (ROIs). This approach has the advantage over voxel-wise statistics to consider also the interactions between the features/voxels. We produce “maps” to visualize the most informative regions of the brain and compare the maps created by our approach with voxel-wise statistics. In classification experiments, using the extracted maps, we achieved classification rates of up to 95.5%.


Statistical Pattern Recognition; Machine Learning and Data Mining; Medical Diagnosis; Medical Image Analysis

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