用注意力机制提升阿尔茨海默病影像诊断准确率，可解释性强。
XDementNET: An Explainable Attention Based Deep Convolutional Network to Detect Alzheimer Progression from MRI data
- 结合多残差块与空间注意力，增强MRI特征提取能力。
- 在多个数据集上实现99%以上分类准确率，最高达100%。
- 支持临床可解释性分析，适合医学AI研究者参考。
阿尔茨海默病是一种常见神经退行性疾病，亟需精准诊断与高效治疗，尤其在医疗成本上升及人工智能广泛应用于医学诊断的背景下。近年来研究表明，结合脑部磁共振成像（MRI）与深度神经网络在阿尔茨海默病诊断中取得显著进展。本文提出一种新型深度学习架构——XDementNET，融合多残差块、专用空间注意力模块、分组查询注意力与多头注意力机制。模型在四个公开数据集上评估了二分类与多分类任务表现。研究还对比了梯度类激活映射（GradCAM）、Score-CAM、Faster Score-CAM与XGRADCAM等可解释性方法。结果表明，本方法持续优于现有技术：在Kaggle数据集上，四分类准确率达99.66%，三分类为99.63%，二分类达100%；在OASIS数据集上分别为99.92%、99.90%、99.95%；在ADNI-1数据集的轴向、矢状面、冠状面及全平面组合上，准确率分别为99.08%、99.85%、99.5%、99.17%；在ADNI-2上分别为97.79%和8.60%。该网络在识别阿尔茨海默病发展阶段方面表现出卓越性能。
原文摘要 · Abstract (English)
A common neurodegenerative disease, Alzheimer's disease requires a precise diagnosis and efficient treatment, particularly in light of escalating healthcare expenses and the expanding use of artificial intelligence in medical diagnostics. Many recent studies shows that the combination of brain Magnetic Resonance Imaging (MRI) and deep neural networks have achieved promising results for diagnosing AD. Using deep convolutional neural networks, this paper introduces a novel deep learning architecture that incorporates multiresidual blocks, specialized spatial attention blocks, grouped query attention, and multi-head attention. The study assessed the model's performance on four publicly accessible datasets and concentrated on identifying binary and multiclass issues across various categories. This paper also takes into account of the explainability of AD's progression and compared with state-of-the-art methods namely Gradient Class Activation Mapping (GradCAM), Score-CAM, Faster Score-CAM, and XGRADCAM. Our methodology consistently outperforms current approaches, achieving 99.66\% accuracy in 4-class classification, 99.63\% in 3-class classification, and 100\% in binary classification using Kaggle datasets. For Open Access Series of Imaging Studies (OASIS) datasets the accuracies are 99.92\%, 99.90\%, and 99.95\% respectively. The Alzheimer's Disease Neuroimaging Initiative-1 (ADNI-1) dataset was used for experiments in three planes (axial, sagittal, and coronal) and a combination of all planes. The study achieved accuracies of 99.08\% for axis, 99.85\% for sagittal, 99.5\% for coronal, and 99.17\% for all axis, and 97.79\% and 8.60\% respectively for ADNI-2. The network's ability to retrieve important information from MRI images is demonstrated by its excellent accuracy in categorizing AD stages.
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