title: Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer's disease.
abstract: 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.
authors.forename: Siqi
authors.initials: S
authors.lastname: Liu
authors.forename: Sidong
authors.initials: S
authors.lastname: Liu
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.forename: Hangyu
authors.initials: H
authors.lastname: Che
authors.forename: Sonia
authors.initials: S
authors.lastname: Pujol
authors.forename: Ron
authors.initials: R
authors.lastname: Kikinis
authors.forename: Dagan
authors.initials: D
authors.lastname: Feng
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fulham
mesh_terms: D000544:Alzheimer Disease / Q000145:classification / Q000175:diagnosis* / Q000473:pathology; D001921:Brain / Q000473:pathology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D059906:Neuroimaging / Q000379:methods*; D060388:Support Vector Machine