Structural MRI and PET imaging play an important role in the diagnosis of Alzheimer's disease (AD), showing the morphological changes and glucose metabolism changes in the brain respectively. The manifestations in the brain image of some cognitive impairment patients are relatively inconspicuous, for example, it still has difficulties in achieving accurate diagnosis through sMRI in clinical practice. With the emergence of deep learning, convolutional neural network (CNN) has become a valuable method in AD-aided diagnosis, but some CNN methods cannot effectively learn the features of brain image, making the diagnosis of AD still presents some challenges. In this work, we propose an end-to-end 3D CNN framework for AD diagnosis based on ResNet, which integrates multi-layer features obtained under the effect of the attention mechanism to better capture subtle differences in brain images. The attention maps showed our model can focus on key brain regions related to the disease diagnosis. Our method was verified in ablation experiments with two modality images on 792 subjects from the ADNI database, where AD diagnostic accuracies of 89.71% and 91.18% were achieved based on sMRI and PET respectively, and also outperformed some state-of-the-art methods.

翻译：结构性MRI和PET成像在阿尔茨海默病诊断中发挥重要作用，分别显示大脑的形态学变化与葡萄糖代谢变化。部分认知障碍患者的脑影像表现相对不明显，例如在临床实践中仍难以通过sMRI实现精确诊断。随着深度学习的兴起，卷积神经网络（CNN）已成为阿尔茨海默病辅助诊断的重要方法，但部分CNN方法无法有效学习脑影像特征，使得阿尔茨海默病诊断仍面临挑战。本文提出一种基于ResNet的端到端3D CNN框架用于阿尔茨海默病诊断，该框架整合了注意力机制作用下获取的多层特征，以更精准地捕捉脑影像中的细微差异。注意力图谱显示，我们的模型能够聚焦与疾病诊断相关的关键脑区。在ADNI数据库792名受试者的两种模态图像消融实验中，基于sMRI和PET的诊断准确率分别达到89.71%和91.18%，且优于部分现有最优方法。