Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images.

ABSTRACT:

Background

Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

Objective

A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN.

Methods

First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre

SUBMITTER: Dong Q 

PROVIDER: S-EPMC7427104 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature