Deep Feature Selection and Causal Analysis of Alzheimer's Disease.

ABSTRACT: Deep convolutional neural networks (DCNNs) have achieved great success for image classification in medical research. Deep learning with brain imaging is the imaging method of choice for the diagnosis and prediction of Alzheimer's disease (AD). However, it is also well known that DCNNs are "black boxes" owing to their low interpretability to humans. The lack of transparency of deep learning compromises its application to the prediction and mechanism investigation in AD. To overcome this limitation, we develop a novel general framework that integrates deep leaning, feature selection, causal inference, and genetic-imaging data analysis for predicting and understanding AD. The proposed algorithm not only improves the prediction accuracy but also identifies the brain regions underlying the deve

SUBMITTER: Liu Y 

PROVIDER: S-EPMC6872503 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature