Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease

Siqi Liu*, Sidong Liu, Weidong Cai, Hangyu Che, Sonia Pujol, Ron Kikinis, Dagan Feng, Michael J. Fulham, ADNI

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

372 Citations (Scopus)


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.

Original languageEnglish
Article number6963480
Pages (from-to)1132-1140
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number4
Publication statusPublished - 1 Apr 2015
Externally publishedYes


  • Alzheimer's Disease
  • Classification
  • Deep Learning
  • MRI
  • Neuroimaging
  • PET


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