融合CNN与ViT的高效模型，提升阿尔茨海默病早期诊断准确率。
EffNetViTLoRA: An Efficient Hybrid Deep Learning Approach for Alzheimer's Disease Diagnosis
- 用CNN和ViT联合提取脑MRI图像的局部与全局特征。
- 在完整ADNI数据集上训练，准确率达92.52%，F1-score为92.76%。
- 引入LoRA微调，适配预训练模型，避免过拟合，适合临床应用。
阿尔茨海默病（AD）是全球最常见的神经退行性疾病之一，其进展会导致认知功能衰退。由于不可逆性，早期诊断对延缓病情至关重要。轻度认知障碍（MCI）介于正常认知（CN）与AD之间，是向痴呆过渡的中间阶段，诊断难度高，因各阶段间差异细微。本文提出EffNetViTLoRA，一种基于全量阿尔茨海默病神经影像计划（ADNI）T1加权磁共振成像（MRI）数据集的端到端通用模型。该模型融合卷积神经网络（CNN）与视觉变换器（ViT），以捕捉MRI图像的局部与全局特征。相比以往仅使用数据子集的研究，本方法在完整数据集上训练，提升模型鲁棒性与临床可靠性。针对大模型微调在源域与目标域差异下表现不佳的问题，引入低秩适应（LoRA）以高效迁移知识，降低过拟合风险。模型在三类诊断任务（AD、MCI、CN）上达到92.52%的分类准确率和92.76%的F1-score。
原文摘要 · Abstract (English)
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders worldwide. As it progresses, it leads to the deterioration of cognitive functions. Since AD is irreversible, early diagnosis is crucial for managing its progression. Mild Cognitive Impairment (MCI) represents an intermediate stage between Cognitively Normal (CN) individuals and those with AD, and is considered a transitional phase from normal cognition to Alzheimer's disease. Diagnosing MCI is particularly challenging due to the subtle differences between adjacent diagnostic categories. In this study, we propose EffNetViTLoRA, a generalized end-to-end model for AD diagnosis using the whole Alzheimer's Disease Neuroimaging Initiative (ADNI) Magnetic Resonance Imaging (MRI) dataset. Our model integrates a Convolutional Neural Network (CNN) with a Vision Transformer (ViT) to capture both local and global features from MRI images. Unlike previous studies that rely on limited subsets of data, our approach is trained on the full T1-weighted MRI dataset from ADNI, resulting in a more robust and unbiased model. This comprehensive methodology enhances the model's clinical reliability. Furthermore, fine-tuning large pretrained models often yields suboptimal results when source and target dataset domains differ. To address this, we incorporate Low-Rank Adaptation (LoRA) to effectively adapt the pretrained ViT model to our target domain. This method enables efficient knowledge transfer and reduces the risk of overfitting. Our model achieves a classification accuracy of 92.52% and an F1-score of 92.76% across three diagnostic categories: AD, MCI, and CN for full ADNI dataset.
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