Combining DTI and MRI for the Automated Detection of Alzheimer’s Disease Using a Large European Multicenter Dataset

2012 ◽  
pp. 18-28 ◽  
Author(s):  
Martin Dyrba ◽  
Michael Ewers ◽  
Martin Wegrzyn ◽  
Ingo Kilimann ◽  
Claudia Plant ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Automated Detection

scholarly journals Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease

NeuroImage ◽  
2010 ◽  
Vol 50 (1) ◽  
pp. 162-174 ◽  
Author(s):  
Claudia Plant ◽  
Stefan J. Teipel ◽  
Annahita Oswald ◽  
Christian Böhm ◽  
Thomas Meindl ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Atrophy ◽  
Automated Detection

Morphological Hippocampal Markers for Automated Detection of Alzheimer's Disease and Mild Cognitive Impairment Converters in Magnetic Resonance Images

2009 ◽  
Vol 17 (3) ◽  
pp. 643-659 ◽  
Author(s):  
Luca Ferrarini ◽  
Giovanni B. Frisoni ◽  
Michela Pievani ◽  
Johan H.C. Reiber ◽  
Rossana Ganzola ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Magnetic Resonance ◽  
Magnetic Resonance Images ◽  
Automated Detection

Automated detection of Alzheimer's disease using bi-directional empirical model decomposition

2020 ◽  
Vol 135 ◽  
pp. 106-113 ◽  
Author(s):  
Joel En Wei Koh ◽  
Vicnesh Jahmunah ◽  
The-Hanh Pham ◽  
Shu Lih Oh ◽  
Edward J Ciaccio ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Empirical Model ◽  
Automated Detection ◽  
Model Decomposition

scholarly journals Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer’s Disease

2020 ◽  
Author(s):  
Ning Wang ◽  
Mingxuan Chen ◽  
K.P. Subbalakshmi
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Automated Detection ◽  
Relative Importance ◽  
Language Abilities ◽  
Attention Networks ◽  
Part Of Speech ◽  
Speech Features ◽  
Learning Architectures

AbstractIn this work we propose three explainable deep learning architectures to automatically detect patients with Alzheimer’s disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional Convolutional Neural Network (CNN) to generate two types of explanations of the model’s action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importance of each of the different features in that class, while the inter-class explanation captures the relative importance between the classes. Note that although we have considered two classes of features in this paper, the architecture is easily expandable to more classes because of its modularity. Extensive experimentations and comparison with several recent models show that our method outperforms these methods with an accuracy of 92.2% and F1 score of 0.952 on the DementiaBank dataset while being able to generate explanations. We show by examples, how to generate these explanations using attention values.

scholarly journals Automated Detection of Amyloid-β-Related Cortical and Subcortical Signal Changes in a Transgenic Model of Alzheimer's Disease using High-Field MRI

2011 ◽  
Vol 23 (2) ◽  
pp. 221-237 ◽  
Author(s):  
Stefan J. Teipel ◽  
Evangelia Kaza ◽  
Stefan Hadlich ◽  
Alexandra Bauer ◽  
Thomas Brüning ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
High Field ◽  
Amyloid Β ◽  
Automated Detection ◽  
High Field Mri ◽  
Transgenic Model ◽  
Model Of Alzheimer’S Disease ◽  
Signal Changes
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