6533b82cfe1ef96bd128feb5
RESEARCH PRODUCT
Convolutional Neural Networks for the Identification of Regions of Interest in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer’s Disease
Markus HubrichAndreas KarwathStefan Kramersubject
Computer sciencebusiness.industryDeep learning05 social sciencesContext (language use)medicine.diseasecomputer.software_genreMachine learningConvolutional neural network03 medical and health sciencesIdentification (information)0302 clinical medicineNeuroimagingVoxelmental disordersmedicineDementia0501 psychology and cognitive sciences050102 behavioral science & comparative psychologyArtificial intelligencebusinesscomputerFeature learning030217 neurology & neurosurgerydescription
When diagnosing patients suffering from dementia based on imaging data like PET scans, the identification of suitable predictive regions of interest (ROIs) is of great importance. We present a case study of 3-D Convolutional Neural Networks (CNNs) for the detection of ROIs in this context, just using voxel data, without any knowledge given a priori. Our results on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) suggest that the predictive performance of the method is on par with that of state-of-the-art methods, with the additional benefit of potential insights into affected brain regions.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2017-01-01 |