用生成模型补全阿尔茨海默病的缺失脑影像，提升诊断准确率。
A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis
- 用GAN从已有模态重建缺失的脑影像数据
- 使阿尔茨海默病分类准确率提升9%
- 适合处理多模态神经影像中数据不全的问题
多模态数据分析因各模态间信息互补，可提高脑疾病诊断准确性。然而，神经影像领域常面临数据不完整问题，部分受试者缺少某些模态数据。传统填补方法如随机采样或零值填充会降低预测精度或引入偏差。本研究提出一种生成对抗网络（GAN）方法，利用已有的结构磁共振成像（T1-weighted MRI）和功能网络连接数据，重建缺失模态，同时保留疾病特征。实验结果表明，相比传统方法，该生成式填补策略在区分阿尔茨海默病患者与认知正常人群时，分类准确率提升了9%。
原文摘要 · Abstract (English)
Multimodal data analysis can lead to more accurate diagnoses of brain disorders due to the complementary information that each modality adds. However, a major challenge of using multimodal datasets in the neuroimaging field is incomplete data, where some of the modalities are missing for certain subjects. Hence, effective strategies are needed for completing the data. Traditional methods, such as subsampling or zero-filling, may reduce the accuracy of predictions or introduce unintended biases. In contrast, advanced methods such as generative models have emerged as promising solutions without these limitations. In this study, we proposed a generative adversarial network method designed to reconstruct missing modalities from existing ones while preserving the disease patterns. We used T1-weighted structural magnetic resonance imaging and functional network connectivity as two modalities. Our findings showed a 9% improvement in the classification accuracy for Alzheimer's disease versus cognitive normal groups when using our generative imputation method compared to the traditional approaches.
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