Title
Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer's Disease.
Abstract
Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimers disease (AD). The current state-of-the-art models for many images analyzing tasks are based on Convolutional Neural Networks (CNN). However, the lack of training data is a common challenge in applying CNN to the diagnosis of AD and its prodromal stages. Another challenge for CAD applications is the controversy between the requiring of longitudinal cortical structural information for higher diagnosis/prognosis accuracy and the computing ability for processing varied imaging features. To address these two challenges, we propose a novel computer-aided AD diagnosis system CNN-Multitask Stochastic Coordinate Coding (MSCC) which integrates CNN with transfer learning strategy, a novel MSCC algorithm and our effective AD-related biomarkers-multivariate morphometry statistics (MMS). We applied the novel CNN-MSCC system on the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset to predict future cognitive clinical measures with baseline Hippocampal/Ventricle MMS features and cortical thickness. The experimental results showed that CNN-MSCC achieved superior results. The proposed system may aid in expediting the diagnosis of AD progress, facilitating earlier clinical intervention, and resulting in improved clinical outcomes.
Year
DOI
Venue
2019
10.1007/978-981-15-1398-5_2
HBAI@IJCAI
Keywords
Field
DocType
Alzheimer’s Disease,Computer-aided Diagnosis,Convolutional Neural Networks (CNN),Multi-task Dictionary Learning,Transfer Learning
CAD,Disease,Convolutional neural network,Computer science,Transfer of learning,Computer-aided diagnosis,Coding (social sciences),Artificial intelligence,Neuroimaging,Cognition,Machine learning
Conference
Volume
Citations 
PageRank 
1072
0
0.34
References 
Authors
4
7
Name
Order
Citations
PageRank
Qunxi Dong102.03
Jie Zhang2841185.41
Qingyang Li3245.09
Paul Thompson43860321.32
Richard J. Caselli55410.20
Jieping Ye66943351.37
Yalin Wang7104279.53