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Journal articles
Alzheimer’s disease early detection from sparse data using brain importance maps
Abstract : 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 “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 map, we achieved classification rates of up to
95.5%.
https://hal.archives-ouvertes.fr/hal-01695694
Contributor : Frédéric Davesne <>
Submitted on : Monday, January 29, 2018 - 4:24:51 PM
Last modification on : Tuesday, June 30, 2020 - 11:56:09 AM
Contributor : Frédéric Davesne <>
Submitted on : Monday, January 29, 2018 - 4:24:51 PM
Last modification on : Tuesday, June 30, 2020 - 11:56:09 AM