S3F: A multi-view slow-fast network for alzheimer's disease diagnosis

Alzheimer's disease (AD) is the most common form of dementia in the elderly. As early detection and diagnosis is imperative for the intervention and prevention of its progression into more detrimental stages, pioneering works have been proposed that use the resting-state functional MRI (rs-fMRI) to identify early mild cognitive impairment (EMCI) based on various convolutional neural networks (CNNs). However the accuracy is not satisfactory. In this paper, we propose a multi-view model based on the SlowFast network, a recently proposed model for video recognition. The rs-fMRI data are treated as videos from three perspectives (i.e. coronal, horizontal and sagittal, corresponding to three anatomical planes in human body) and the jointly learned hierarchical representations are fused in the fully connected layer. We examine our model on a publicly accessible Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our method significantly outperforms other competing methods and achieves state-of-the-art accuracy. Besides, we also provide a baseline on the classification task over all clinical phases of AD.