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：结构磁共振成像（sMRI）广泛用于脑神经系统疾病的诊断；而纵向MRI通常用于监测和捕捉疾病进展，正如临床诊断阿尔茨海默病（AD）时所采用的方法。然而，当前大多数方法忽略了AD的渐进性，仅基于单次sMRI进行AD识别。本文考虑利用受试者的纵向MRI进行AD识别的问题。为捕捉sMRI中的纵向变化，我们提出了一种新颖模型Longformer，这是一种时空Transformer网络，它通过注意力机制在空间上处理每个时间点的sMRI，并随时间整合脑区特征，从而获得用于分类的纵向嵌入。在ADNI数据集上，我们的Longformer在区分AD不同阶段的二分类任务中达到了最先进的性能。我们的源代码可在https://github.com/Qybc/LongFormer获取。