2017
Karwath, Andreas; Hubrich, Markus; Kramer, Stefan
Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings, Springer Springer International Publishing, Cham, 2017, ISBN: 978-3-319-59758-4.
Abstract | Links | BibTeX | Tags: alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization
@conference{karwath2017a,
title = {Convolutional Neural Networks for the Identification of Regions of Interests in PET Scans: A Study of Representation Learning for Diagnosing Alzheimer's Disease},
author = {Andreas Karwath and Markus Hubrich and Stefan Kramer},
editor = {en Teije, Annette and Popow, Christian and Holmes, John H. and Sacchi, Lucia},
url = {http://dx.doi.org/10.1007/978-3-319-59758-4_36},
doi = {10.1007/978-3-319-59758-4_36},
isbn = {978-3-319-59758-4},
year = {2017},
date = {2017-06-21},
urldate = {2017-06-21},
booktitle = {Artificial Intelligence in Medicine: 16th Conference on Artificial Intelligence in Medicine, AIME 2017, Vienna, Austria, June 21-24, 2017, Proceedings},
pages = {316-321},
publisher = {Springer International Publishing},
address = {Cham},
organization = {Springer},
abstract = {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.},
keywords = {alzheimer, artificial intelligence, deep learning, health data science, machine learning, medicine, visualization},
pubstate = {published},
tppubtype = {conference}
}
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.