0000000000704388

AUTHOR

Markus Hubrich

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Convolutional Neural Networks for the Identification of Regions of Interest in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer…

2017

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.

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 & neurosurgery
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