Amyotrophic Lateral Sclerosis (ALS) is a complex neurodegenerative disorder involving motor neuron degeneration. Significant research has begun to establish brain magnetic resonance imaging (MRI) as a potential biomarker to diagnose and monitor the state of the disease. Deep learning has turned into a prominent class of machine learning programs in computer vision and has been successfully employed to solve diverse medical image analysis tasks. However, deep learning-based methods applied to neuroimaging have not achieved superior performance in ALS patients classification from healthy controls due to having insignificant structural changes correlated with pathological features. Therefore, the critical challenge in deep models is to determine useful discriminative features with limited training data. By exploiting the long-range relationship of image features, this study introduces a framework named SF2Former that leverages vision transformer architecture's power to distinguish the ALS subjects from the control group. To further improve the network's performance, spatial and frequency domain information are combined because MRI scans are captured in the frequency domain before being converted to the spatial domain. The proposed framework is trained with a set of consecutive coronal 2D slices, which uses the pre-trained weights on ImageNet by leveraging transfer learning. Finally, a majority voting scheme has been employed to those coronal slices of a particular subject to produce the final classification decision. Our proposed architecture has been thoroughly assessed with multi-modal neuroimaging data using two well-organized versions of the Canadian ALS Neuroimaging Consortium (CALSNIC) multi-center datasets. The experimental results demonstrate the superiority of our proposed strategy in terms of classification accuracy compared with several popular deep learning-based techniques.

翻译：肌萎缩侧索硬化症（ALS）是一种涉及运动神经元退化的复杂神经退行性疾病。大量研究已开始将脑部磁共振成像（MRI）确立为诊断和监测疾病状态的潜在生物标志物。深度学习已成为计算机视觉领域机器学习程序的重要分支，并被成功应用于解决多种医学图像分析任务。然而，由于与病理特征相关的结构变化不显著，基于深度学习的神经影像学方法在ALS患者与健康对照的分类中尚未取得优越性能。因此，深度模型的关键挑战在于利用有限的训练数据确定有效的判别性特征。通过利用图像特征的长程依赖关系，本研究提出了一种名为SF2Former的框架，该框架借助视觉Transformer架构的能力来区分ALS受试者与对照组。为了进一步提升网络性能，我们将空间域与频率域信息进行融合——因为MRI扫描在转换为空间域之前，是在频率域中采集的。所提出的框架采用一组连续的冠状二维切片进行训练，并通过迁移学习使用ImageNet上的预训练权重。最后，针对特定受试者的冠状切片采用多数投票机制以产生最终分类决策。我们提出的架构已使用加拿大ALS神经影像联盟（CALSNIC）多中心数据集的两个规范化版本，在多模态神经影像数据上进行了全面评估。实验结果表明，与几种流行的基于深度学习的技术相比，我们的策略在分类准确率方面表现出优越性。