AIMC Topic: Alzheimer Disease
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Multimodal Data Fusion of Deep Learning and Dynamic Functional Connectivity Features to Predict Alzheimer's Disease Progression.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Early prediction of diseased brain conditions is critical for curing illness and preventing irreversible neuronal dysfunction and loss. Generically regarding the different neuroimaging modalities as filtered, complementary insights of brain's anatomi...
Alzheimer's Disease Brain Network Classification Using Improved Transfer Feature Learning with Joint Distribution Adaptation.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Alzheimer's disease significantly affects the quality of life of patients. This paper proposes an approach to identify Alzheimer's disease based on transfer learning using functional MRI images, which is especially useful when the training dataset is...
[Artificial Intelligence for Diagnostic Support of Neuroimage].
Brain and nerve = Shinkei kenkyu no shinpo
Jul 1, 2019
Artificial intelligence (AI) shows promises in terms of diagnostic support on neuroimaging. We developed a software that predicts Alzheimer's disease (AD) using support vector machines (SVM) through three-dimensional brain MR images. Here, we will ex...
An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures.
Journal of psychiatry & neuroscience : JPN
Jul 1, 2019
BACKGROUND: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal stages. Many neuroimaging studies have focused on case–control classification and predicting conver...
Transfer learning on T1-weighted images for brain age estimation.
Mathematical biosciences and engineering : MBE
May 18, 2019
Due to both the hidden nature and the irreversibility of Alzheimers disease (AD), it has become the killer of the elderly and is thus the focus of much attention in the medical field. Radiologists compare the predicted brain age with the ground truth...
The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease.
Neuroinformatics
Apr 1, 2019
A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that such biomarkers can be obtained by image analysis techniques. However, these techniques cannot be dire...
Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer's Disease.
Neuroinformatics
Apr 1, 2019
Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as pre...
Deep Learning for Alzheimer's Disease Classification using Texture Features.
Current medical imaging reviews
Jan 1, 2019
BACKGROUND: We propose a classification method for Alzheimer's disease (AD) based on the texture of the hippocampus, which is the organ that is most affected by the onset of AD.
Alzheimer's Disease Classification Based on Multi-feature Fusion.
Current medical imaging reviews
Jan 1, 2019
BACKGROUND: In this study, we investigated the fusion of texture and morphometric features as a possible diagnostic biomarker for Alzheimer's Disease (AD).
Volumetric Histogram-Based Alzheimer's Disease Detection Using Support Vector Machine.
Journal of Alzheimer's disease : JAD
Jan 1, 2019
In this research work, machine learning techniques are used to classify magnetic resonance imaging brain scans of people with Alzheimer's disease. This work deals with binary classification between Alzheimer's disease and cognitively normal. Supervis...
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