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Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease

Institution:
1Biomedical and Multimedia Information Technology Research Group, School of Information Technologies, University of Sydney, Sydney, N.S.W., 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:
Apr-2015
Journal:
IEEE Trans Biomed Eng
Volume Number:
62
Issue Number:
4
Pages:
1132-40
Citation:
IEEE Trans Biomed Eng. 2015 Apr;62(4):1132-40.
PubMed ID:
25423647
PMCID:
PMC4394860
Keywords:
Alzheimer’s Disease, Classification, Deep Learning, MRI, Neuroimaging
Appears in Collections:
NAC, NA-MIC, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Liu S., Liu S., Cai W., Che H., Pujol S., Kikinis R., Feng D., Fulham M.J., ADNI . Multimodal Neuroimaging Feature Learning for Multiclass Diagnosis of Alzheimer's Disease. IEEE Trans Biomed Eng. 2015 Apr;62(4):1132-40. PMID: 25423647. PMCID: PMC4394860.
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The accurate diagnosis of Alzheimer's disease (AD) is essential for patient care and will be increasingly important as disease modifying agents become available, early in the course of the disease. Although studies have applied machine learning methods for the computer-aided diagnosis of AD, a bottleneck in the diagnostic performance was shown in previous methods, due to the lacking of efficient strategies for representing neuroimaging biomarkers. In this study, we designed a novel diagnostic framework with deep learning architecture to aid the diagnosis of AD. This framework uses a zero-masking strategy for data fusion to extract complementary information from multiple data modalities. Compared to the previous state-of-the-art workflows, our method is capable of fusing multimodal neuroimaging features in one setting and has the potential to require less labeled data. A performance gain was achieved in both binary classification and multiclass classification of AD. The advantages and limitations of the proposed framework are discussed.

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