The Publication Database hosted by SPL
|
All Publications
Upload
Advanced Search
Gallery View
Download Statistics
Help
Import
Log in
Localized Sparse Code Gradient in Alzheimer's Disease Staging
|
|
Institution: |
1Biomedical & Multimedia Information Technology (BMIT) Research Group, University of Sydney, Sydney, NSW, Australia. 2Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard medical School, Boston, MA, USA. |
Publisher: |
IEEE Engineering in Medicine and Biology Society |
Publication Date: |
Jul-2013 |
Journal: |
Conf Proc IEEE Eng Med Biol Soc |
Volume Number: |
2013 |
Pages: |
5398-401 |
Citation: |
Conf Proc IEEE Eng Med Biol Soc. 2013 Jul; 2013:5398-401. |
PubMed ID: |
24110956 |
PMCID: |
PMC4849882 |
Appears in Collections: |
NA-MIC, NAC, SLICER, SPL |
Sponsors: |
P41 RR013218/RR/NCRR NIH HHS/United States U54 EB005149/EB/NIBIB NIH HHS/United States P41 EB015902/EB/NIBIB NIH HHS/United States |
Generated Citation: |
Liu S., Cai W., Song Y., Pujol S., Kikinis R., Wen L., Feng D.D. Localized Sparse Code Gradient in Alzheimer's Disease Staging. Conf Proc IEEE Eng Med Biol Soc. 2013 Jul; 2013:5398-401. PMID: 24110956. PMCID: PMC4849882. |
| Downloaded: | 682 times. [view map] |
| Paper: | Download, View online |
| Export citation: | |
| Google Scholar: | link |
The accurate diagnosis of Alzheimer's disease (AD) at different stages is essential to identify patients at high risk of dementia and plan prevention or treatment measures accordingly. In this study, we proposed a new AD staging method for the entire spectrum of AD including the AD, Mild Cognitive Impairment with and without AD conversions, and Cognitive Normal groups. Our method embedded the high dimensional multi-view features derived from neuroimaging data into a low dimensional feature space and could form a more distinctive representation than the naive concatenated features. It also updated the testing data based on the Localized Sparse Code Gradients (LSCG) to further enhance the classification. The LSCG algorithm, validated using Magnetic Resonance Imaging data from the ADNI baseline cohort, achieved significant improvements on all diagnosis groups compared to using the original sparse coding method.
Additional Material
1 File (134.472kB)
Liu-EMBS2013-fig1.jpg (134.472kB)