Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs

Many neurological diseases are characterized by gradual deterioration of brain structure andfunction. Large longitudinal MRI datasets have revealed such deterioration, in part, by applying machine and deep learning to predict diagnosis. A popular approach is to apply Convolutional Neural Networks (C...

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Bibliographic Details
Main Authors:	Adeli, E. (Author), Ouyang, J. (Author), Pfefferbaum, A. (Author), Pohl, K.M (Author), Sullivan, E.V (Author), Tapert, S.F (Author), Zhao, Q. (Author)
Format:	Article
Language:	English
Published:	Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:	accuracy Accurate tracking adolescent Adolescent alcoholism Alzheimer disease Alzheimer Disease Alzheimer's disease Article Brain structure controlled study Convolutional neural networks data consistency deep learning Deterioration Diagnosis diagnostic imaging disease classification disease exacerbation Disease progression Disease Progression feature extraction female human Humans Large dataset longitudinal analysis longitudinal study Magnetic Resonance Imaging major clinical study male Neural Networks, Computer Neurodegenerative diseases Neurodevelopment neuroimaging Neuroimaging Neurological disease Normal controls nuclear magnetic resonance imaging recurrent neural network Recurrent neural network (RNNs) recurrent neural networks Recurrent neural networks
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Longitudinal Pooling & Consistency Regularization to Model Disease Progression from MRIs

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