A Multi-branch Attention-based Deep Learning Method for ALS Identification with sMRI Data

The structural Magnetic resonance imaging (sMRI) of spinal cord plays a significant role in the clinical diagnosis of Amyotrophic Lateral Sclerosis (ALS). But due to small cross-sectional area in the axial plane and long sagittal/coronal expansion of spinal cord, the diagnosis of ALS using sMRI of spinal cord has remained largely at the stage of morphological observation. In this study, a Multi-branch attention-based deep learning method is proposed to solve this problem. Multi-branch framework is utilized to extract general features of all levels of spinal cord for challenging of long sagittal and coronal expansion of spinal cord, and attention module coupled with multi-scale module in each branch is applied to extract multi-scale features and pay more attention to the important regions of the spinal cord in the axial plane. Experiments show that the proposed method obtains better performance in ALS identification, which implies that the proposed method can extract features of important region in the spinal cord and could be helpful to find more regions sensitive for ALS disease identification.