A Lightweight Spatial Attention Module with Adaptive Receptive Fields in 3D Convolutional Neural Network for Alzheimer’s Disease Classification

Abstract

The development of deep learning provides powerful support for disease classification of neuroimaging data. However, in the classification of neuroimaging data based on deep learning methods, the spatial information cannot be fully utilized. In this paper, we propose a lightweight 3D spatial attention module with adaptive receptive fields, which allows neurons to adaptively adjust the receptive field size according to multiple scales of input information. The attention module can fuse spatial information of different scales on multiple branches, so that 3D spatial information of neuroimaging data can be fully utilized. A 3D-ResNet18 based on our proposed attention module is trained to diagnose Alzheimer’s disease (AD). Experiments are conducted on 521 subjects (254 of patients with AD and 267 of normal controls) from Alzheimer’s Disease National Initiative (ADNI) dataset of 3D structural MRI brain scans. Experimental results show the effectiveness and efficiency of our proposed approach for AD classification.