Longitudinal Pooling & Consistency Regularization to Model Disease Progression From MRIs.

ABSTRACT: 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 (CNN) to extract informative features from each visit of the longitudinal MRI and then use those features to classify each visit via Recurrent Neural Networks (RNNs). Such modeling neglects the progressive nature of the disease, which may result in clinically implausible classifications across visits. To avoid this issue, we propose to combine features across visits by coupling feature extraction with a novel longitudinal pooling layer and enforce consistency of the classification across visits in

SUBMITTER: Ouyang J 

PROVIDER: S-EPMC8221531 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature