Longformer: Longitudinal Transformer for Alzheimer's Disease Classification with Structural MRIs

Abstract

Structural magnetic resonance imaging (sMRI) is widely used for brain neurological disease diagnosis; while longitudinal MRIs are often collected to monitor and capture disease progression, as clinically used in diagnosing Alzheimer's disease (AD). However, most current methods neglect AD's progressive nature and only take a single sMRI for recognizing AD. In this paper, we consider the problem of leveraging the longitudinal MRIs of a subject for AD identification. To capture longitudinal changes in sMRIs, we propose a novel model Longformer, a spatiotemporal transformer network that performs attention mechanisms spatially on sMRIs at each time point and integrates brain region features over time to obtain longitudinal embeddings for classification. Our Longformer achieves state-of-the-art performance on two binary classification tasks of separating different stages of AD using the ADNI dataset. Our source code is available at https://github.com/Qybc/LongFormer.