提出首个图扩散模型，用于从源脑图预测目标脑图
Graph Residual Noise Learner Network for Brain Connectivity Graph Prediction
- 基于图扩散框架，学习残差噪声以重建脑连接图
- 在缺失数据场景下提升图预测精度，优于传统GAN方法
- 适合神经疾病诊断与小样本脑图补全研究者使用
描绘脑连接指纹的形态学脑图对刻画脑功能连接异常至关重要。由于神经影像处理流程耗时且不完整，此类数据常存在缺失。因此，从源图预测目标图对于以最少数据资源诊断神经疾病极为关键。尽管已有多种脑图生成模型取得良好效果，但多基于生成对抗网络（GAN），存在模式崩溃且需大量训练数据的问题。扩散模型因具备稳定训练目标和易扩展性，可缓解上述问题。然而，将扩散过程应用于图边会破坏脑连接矩阵的拓扑对称性。为此，我们提出首个用于从源图预测目标图的图扩散模型——图残差噪声学习网络（Grenol-Net）。
原文摘要 · Abstract (English)
A morphological brain graph depicting a connectional fingerprint is of paramount importance for charting brain dysconnectivity patterns. Such data often has missing observations due to various reasons such as time-consuming and incomplete neuroimage processing pipelines. Thus, predicting a target brain graph from a source graph is crucial for better diagnosing neurological disorders with minimal data acquisition resources. Many brain graph generative models were proposed for promising results, yet they are mostly based on generative adversarial networks (GAN), which could suffer from mode collapse and require large training datasets. Recent developments in diffusion models address these problems by offering essential properties such as a stable training objective and easy scalability. However, applying a diffusion process to graph edges fails to maintain the topological symmetry of the brain connectivity matrices. To meet these challenges, we propose the Graph Residual Noise Learner Network (Grenol-Net), the first graph diffusion model for predicting a target graph from a source graph.
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