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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.
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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
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