TY - GEN
T1 - Multi-task Dictionary Learning Based on Convolutional Neural Networks for Longitudinal Clinical Score Predictions in Alzheimer’s Disease
AU - for the Alzheimer’s Disease Neuroimaging Initiative
AU - Dong, Qunxi
AU - Zhang, Jie
AU - Li, Qingyang
AU - Thompson, Pau M.
AU - Caselli, Richard J.
AU - Ye, Jieping
AU - Wang, Yalin
N1 - Publisher Copyright:
© 2019, Springer Nature Singapore Pte Ltd.
PY - 2019
Y1 - 2019
N2 - Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimer’s 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-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 Alzheimer’s 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.
AB - Computer-aided diagnosis (CAD) systems for medical images are seen as effective tools to improve the efficiency of diagnosis and prognosis of Alzheimer’s 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-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 Alzheimer’s 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.
KW - Alzheimer’s Disease
KW - Computer-aided diagnosis
KW - Convolutional Neural Networks (CNN)
KW - Multi-task dictionary learning
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85076963689&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076963689&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-1398-5_2
DO - 10.1007/978-981-15-1398-5_2
M3 - Conference contribution
AN - SCOPUS:85076963689
SN - 9789811513978
T3 - Communications in Computer and Information Science
SP - 21
EP - 35
BT - Human Brain and Artificial Intelligence - 1st International Workshop, HBAI 2019, held in Conjunction with IJCAI 2019, Revised Selected Papers
A2 - Zeng, An
A2 - Pan, Dan
A2 - Hao, Tianyong
A2 - Zhang, Daoqiang
A2 - Shi, Yiyu
A2 - Song, Xiaowei
PB - Springer
T2 - 1st International Workshop on Human Brain and Artificial Intelligence, HBAI 2019, held in conjunction with the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
Y2 - 12 August 2019 through 12 August 2019
ER -