Analysis of brain NMR images for age estimation with deep learning
During the last decade, deep learning and Convolutional Neural Networks (CNNs) have
produced a devastating impact on computer vision, yielding exceptional results on a variety …
produced a devastating impact on computer vision, yielding exceptional results on a variety …
From a deep learning model back to the brain-inferring morphological markers and their relation to aging
Deep convolutional neural networks (CNN) enabled a major leap in image processing tasks
including brain imaging analysis. In this work, we present a Deep Learning framework for …
including brain imaging analysis. In this work, we present a Deep Learning framework for …
Comparison of machine learning models for brain age prediction using six imaging modalities on middle-aged and older adults
Machine learning (ML) has transformed neuroimaging research by enabling accurate
predictions and feature extraction from large datasets. In this study, we investigate the …
predictions and feature extraction from large datasets. In this study, we investigate the …
Federation of brain age estimation in structural neuroimaging data
Brain age estimation is a widely used approach to evaluate the impact of various
neurological or psychiatric brain disorders on the brain developmental or aging process …
neurological or psychiatric brain disorders on the brain developmental or aging process …
Alzheimer's Disease Detection via a Surrogate Brain Age Prediction Task using 3D Convolutional Neural Networks
C Zheng, B Pfahringer, M Mayo - 2022 International Joint …, 2022 - ieeexplore.ieee.org
Structural magnetic resonance imaging (MRI) studies demonstrated that Alzheimer's
Disease (AD) causes not only local but also whole-brain level neural degenerative changes …
Disease (AD) causes not only local but also whole-brain level neural degenerative changes …
Predem: A computational framework for prediction of early dementia using Deep Neural Networks
D Sharma, N Soni, B Devi, VG Shankar - Procedia Computer Science, 2022 - Elsevier
This paper is focused on creating deep neural networks for the prediction and determination
of dementia. It can be used in healthcare, research, and industrial applications. Impairment …
of dementia. It can be used in healthcare, research, and industrial applications. Impairment …
Prediction of Alzheimer's disease progression with multi-information generative adversarial network
Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its long-term
progression prediction is definitely important. The structural Magnetic Resonance Imaging …
progression prediction is definitely important. The structural Magnetic Resonance Imaging …
Convolutional neural-network-based ordinal regression for brain age prediction from MRI scans
K Sokolova, GJ Barker… - Medical Imaging 2020 …, 2020 - spiedigitallibrary.org
Throughout human lifetime there are various ageing-related changes occurring. It has been
demonstrated that departures from the healthy ageing trajectory can be used as a biomarker …
demonstrated that departures from the healthy ageing trajectory can be used as a biomarker …
A Deep Convolutional Neural Network For Early Diagnosis of Alzheimer's Disease
M Liu, MY Shalaginov, R Liao… - 2022 IEEE-EMBS …, 2022 - ieeexplore.ieee.org
Alzheimer's disease is a neurologic disorder that hinders many elderly people from being
able to live fulfilling lives. There is no cure for this disease, but patients can get medication to …
able to live fulfilling lives. There is no cure for this disease, but patients can get medication to …
Improved prediction of brain age using multimodal neuroimaging data
Brain age prediction based on imaging data and machine learning (ML) methods has great
potential to provide insights into the development of cognition and mental disorders. Though …
potential to provide insights into the development of cognition and mental disorders. Though …