混合注意力和多尺度模块的阿尔茨海默病分类方法
收稿日期: 2024-10-14
网络出版日期: 2025-01-14
基金资助
上海市自然科学基金资助项目(18ZR1426900)
Hybrid Attention and Multiscale Module for Alzheimer's Disease Classification
Received date: 2024-10-14
Online published: 2025-01-14
阿尔茨海默病是痴呆症中最常见的一种神经退行性疾病，其病程进展慢、影像学特征复杂多样，传统影像的阅片诊断过程非常耗时且准确率判断差异大．针对这一问题，本文提出了一种基于混合注意力和多尺度信息融合的分类方法（3D HAMSNet）．该方法基于影像数据，利用卷积神经网络，通过引入混合注意力机制增强模型对海马体、杏仁核和颞叶等区域的关注，并利用基于空洞卷积和软注意力的多尺度信息融合模块有效融合阿尔茨海默病的多种空间尺度特征，从而提高对阿尔茨海默病的早期诊断和预测能力．在198名阿尔茨海默病患者、200名轻度认知障碍患者和139名健康对照组的三分类任务中，所提出的方法分类准确率、特异性和F1分数分别达到了 94.14%、97.07%和94.17%，相较于基线网络分别提升了9.88%、4.94%和10.17%．该方法相较现有分类方法表现突出，为阿尔茨海默病的早期诊断提供了新的方法．
顾佳佳 , 王远军 . 混合注意力和多尺度模块的阿尔茨海默病分类方法[J]. 波谱学杂志, 2025 , 42(2) : 103 -116 . DOI: 10.11938/cjmr20243132
Alzheimer's disease is the most common neurodegenerative disorder among dementia, characterized by slow disease progression and complex imaging features. Traditional image-based diagnostic processes are time-consuming and vary in accuracy. To address these challenges, this study proposes a novel classification method based on hybrid attention and multi-scale information fusion (3D HAMSNet). The method leverages image data and a convolutional neural network to enhance the model's attention to the hippocampus, amygdala, and temporal lobe through the introduction of a hybrid attention mechanism. Additionally, it integrates multiscale spatial scale features of Alzheimer's disease by using a multiscale information fusion module based on dilated convolution and soft attention, enhancing early diagnosis and prediction. Finally, tested on 198 Alzheimer's patients, 200 individuals with mild cognitive impairment, and 139 healthy controls, it achieved 94.14% accuracy, 97.07% specificity, and 94.17% F1 score—represented improvements of 9.88%, 4.94%, and 10.17% over the baseline. The method outperforms existing classification methods and provides a new approach for early Alzheimer's diagnosis.
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