Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, 528437, China.
Research Center for Medical Artificial Intelligence, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Sci Rep. 2024 Oct 16;14(1):24210. doi: 10.1038/s41598-024-74508-z.
Convolutional neural networks (CNNs) for extracting structural information from structural magnetic resonance imaging (sMRI), combined with functional magnetic resonance imaging (fMRI) and neuropsychological features, has emerged as a pivotal tool for early diagnosis of Alzheimer's disease (AD). However, the fixed-size convolutional kernels in CNNs have limitations in capturing global features, reducing the effectiveness of AD diagnosis. We introduced a group self-calibrated coordinate attention network (GSCANet) designed for the precise diagnosis of AD using multimodal data, including encompassing Haralick texture features, functional connectivity, and neuropsychological scores. GSCANet utilizes a parallel group self-calibrated module to enhance original spatial features, expanding the field of view and embedding spatial data into channel information through a coordinate attention module, which ensures long-term contextual interaction. In a four-classification comparison (AD vs. early MCI (EMCI) vs. late MCI (LMCI) vs. normal control (NC)), GSCANet demonstrated an accuracy of 78.70%. For the three-classification comparison (AD vs. MCI vs. NC), it achieved an accuracy of 83.33%. Moreover, our method exhibited impressive accuracies in the AD vs. NC (92.81%) and EMCI vs. LMCI (84.67%) classifications. GSCANet improves classification performance at different stages of AD by employing group self-calibrated to expand features receptive field and integrating coordinated attention to facilitate significant interactions among channels and spaces. Providing insights into AD mechanisms and showcasing scalability for various disease predictions.
卷积神经网络 (CNN) 可从结构磁共振成像 (sMRI) 中提取结构信息，结合功能磁共振成像 (fMRI) 和神经心理学特征，已成为阿尔茨海默病 (AD) 早期诊断的重要工具。然而，CNN 中的固定大小卷积核在捕获全局特征方面存在局限性，降低了 AD 诊断的效果。我们引入了一种组自校准坐标注意力网络 (GSCANet)，用于使用多模态数据（包括哈拉斯纹理特征、功能连接和神经心理学评分）进行 AD 的精确诊断。GSCANet 利用并行组自校准模块增强原始空间特征，通过坐标注意力模块将视场扩展并将空间数据嵌入通道信息中，从而确保长期的上下文交互。在四类分类比较（AD 与早期轻度认知障碍 (EMCI) 与晚期轻度认知障碍 (LMCI) 与正常对照组 (NC)）中，GSCANet 的准确率为 78.70%。在三类分类比较（AD 与 MCI 与 NC）中，它的准确率为 83.33%。此外，我们的方法在 AD 与 NC（92.81%）和 EMCI 与 LMCI（84.67%）的分类中表现出令人印象深刻的准确率。GSCANet 通过采用组自校准来扩展特征感受野，并整合协调注意力来促进通道和空间之间的显著交互，从而提高了不同 AD 阶段的分类性能。为 AD 机制提供了深入的了解，并展示了对各种疾病预测的可扩展性。
