融合影像与基因数据，用图模型区分阿尔茨海默病三阶段。
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection
- 构建基因与影像的异构二分图，学习联合表示。
- 小样本下准确区分正常、轻度痴呆和阿尔茨海默病三类人群。
- 可识别各阶段关键致病基因，适用于多疾病研究。
影像与基因数据提供互补且丰富的特征，其融合有助于揭示复杂疾病的内在机制。本研究提出一种新方法，利用结构MRI图像与基因表达数据进行阿尔茨海默病（AD）检测。框架构建包含基因与图像两类节点的异构二分图，实现对AD、轻度认知障碍（MCI）及认知正常（CN）三类状态的有效分类，且在小样本条件下表现良好。同时，模型能识别在各类别中起关键作用的基因。通过准确率、召回率、精确率和F1分数等指标评估性能，结果表明该方法具备向其他放射基因组学疾病分类任务拓展的潜力。
原文摘要 · Abstract (English)
Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases.
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