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.

翻译：结构性磁共振成像(sMRI)广泛用于脑神经系统疾病诊断；而纵向MRI通常用于监测和捕捉疾病进展，临床上常用于阿尔茨海默病(AD)的诊断。然而，现有方法大多忽视了AD的渐进特性，仅通过单次sMRI进行AD识别。本文研究了利用受试者纵向MRI识别AD的问题。为捕捉sMRI中的纵向变化，我们提出了一种新颖模型Longformer——一种时空Transformer网络，它在每个时间点对sMRI执行空间注意力机制，并通过时间维度整合脑区特征以获得用于分类的纵向嵌入。在ADNI数据集上，我们的Longformer在两个区分AD不同阶段的二分类任务中达到了最先进性能。我们的源代码发布于https://github.com/Qybc/LongFormer。