Abstract
In Alzheimer’s research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer’s. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention and diagnosis of Alzheimer’s. Traditional methods use various classification models to distinguish MCI converters from non-converters, while the performance is usually limited by the small number of available data. Moreover, previous methods only use the data at baseline time for training but ignore the longitudinal information at other time points along the disease progression. To tackle with these problems, we propose a novel deep learning framework that uncovers the temporal correlation structure between adjacent time points in the disease progression. We also construct a generative framework to learn the inherent data distribution so as to produce more reliable data to strengthen the training process. Extensive experiments on the ADNI cohort validate the superiority of our model.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings |
Editors | Alejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel |
Publisher | Springer Verlag |
Pages | 446-454 |
Number of pages | 9 |
ISBN (Print) | 9783030009304 |
DOIs | |
Publication status | Published - 2018 |
Event | 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 20 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11072 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Other
Other | 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 |
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Country/Territory | Spain |
City | Granada |
Period | 18/9/16 → 18/9/20 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2018.
Keywords
- Alzheimer’s disease
- Deep learning
- MCI conversion prediction
- Temporal correlation structure
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science