用T1影像生成缺失的DWI影像，提升阿尔茨海默病分类准确率。
Multi-modal Imputation for Alzheimer's Disease Classification
- 基于条件去噪扩散模型，从T1图合成缺失的DWI图。
- 多模态融合后对少数类（如轻度认知障碍）分类精度显著提升。
- 适用于影像不全的临床数据，适合医学图像补全研究者。
深度学习在利用磁共振成像（MRI）预测神经退行性疾病（如阿尔茨海默病）方面已取得成功。结合多种成像模态（如T1加权成像和弥散加权成像）可提升诊断性能，但完整多模态数据集并不总可用。本文采用条件去噪扩散概率模型，从T1扫描中生成缺失的DWI扫描。通过大量实验评估该补全方法对单模态与双模态深度学习模型在三分类任务（正常认知、轻度认知障碍、阿尔茨海默病）中的影响。结果显示，在多种补全配置下，多个指标均有提升，尤其在少数类敏感指标上表现更优。
原文摘要 · Abstract (English)
Deep learning has been successful in predicting neurodegenerative disorders, such as Alzheimer's disease, from magnetic resonance imaging (MRI). Combining multiple imaging modalities, such as T1-weighted (T1) and diffusion-weighted imaging (DWI) scans, can increase diagnostic performance. However, complete multimodal datasets are not always available. We use a conditional denoising diffusion probabilistic model to impute missing DWI scans from T1 scans. We perform extensive experiments to evaluate whether such imputation improves the accuracy of uni-modal and bi-modal deep learning models for 3-way Alzheimer's disease classification-cognitively normal, mild cognitive impairment, and Alzheimer's disease. We observe improvements in several metrics, particularly those sensitive to minority classes, for several imputation configurations.
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