融合结构与功能脑影像，提升阿尔茨海默病分类精度
Multimodal Deep Learning for Dynamic and Static Neuroimaging: Integrating MRI and fMRI for Alzheimer Disease Analysis
- 用3D CNN和LSTM分别提取脑部结构与活动特征，再融合进行联合分析
- 数据增强显著提升小样本多模态模型的分类稳定性与泛化能力
- 多模态模型对数据量敏感，单模态大样本下增强效果不明显
磁共振成像（MRI）提供精细的脑结构信息，功能磁共振成像（fMRI）则捕捉脑区随时间的活动变化。本文提出一种多模态深度学习框架，融合MRI与fMRI实现阿尔茨海默病（AD）、轻度认知障碍及正常认知状态的多类别分类。利用3D卷积神经网络从MRI中提取结构特征，通过递归网络从fMRI序列中学习时序特征，并进行特征融合以实现时空联合建模。在包含29名受试者的小规模配对MRI-fMRI数据集上，对比了有无数据增强的实验结果。结果显示，数据增强显著提升了多模态3DCNN-LSTM模型的分类稳定性和泛化性能；而在大规模单模态MRI数据集上，增强效果不明显。这表明，在基于神经影像的阿尔茨海默病分类中，数据量与模态类型对增强策略的设计具有决定性影响。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) provides detailed structural information, while functional MRI (fMRI) captures temporal brain activity. In this work, we present a multimodal deep learning framework that integrates MRI and fMRI for multi-class classification of Alzheimer Disease (AD), Mild Cognitive Impairment, and Normal Cognitive State. Structural features are extracted from MRI using 3D convolutional neural networks, while temporal features are learned from fMRI sequences using recurrent architectures. These representations are fused to enable joint spatial-temporal learning. Experiments were conducted on a small paired MRI-fMRI dataset (29 subjects), both with and without data augmentation. Results show that data augmentation substantially improves classification stability and generalization, particularly for the multimodal 3DCNN-LSTM model. In contrast, augmentation was found to be ineffective for a large-scale single-modality MRI dataset. These findings highlight the importance of dataset size and modality when designing augmentation strategies for neuroimaging-based AD classification.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。