论文标题
GSR-NET：用于从低分辨率功能性脑连接组预测高分辨率的图形超分辨率网络
GSR-Net: Graph Super-Resolution Network for Predicting High-Resolution from Low-Resolution Functional Brain Connectomes
论文作者
论文摘要
为了图像超分辨率（SR）而量身定制了吸引人但严格的深度学习体系结构，但是，这些结构未能推广到非欧几里得数据（例如脑连接体）。具体而言，以更高分辨率（HR）（即，添加新的图节点/边缘）以高分辨率（LR）脑连接组的超级分辨模型建立生成模型，尽管这将阐明需要昂贵的数据收集和对解剖学大脑区域进行手动标记的需求（即parcellation）。为了填补此空白，我们介绍了GSR-NET（图形超分辨率网络），这是第一个在图形结构数据上运行的超分辨率框架，该数据从低分辨率图中生成了高分辨率的脑图。首先，我们基于图形卷积，汇总和非欧盟数据特定的UCLONGING操作采用U-NET类似U-NET。 However, unlike conventional U-Nets where graph nodes represent samples and node features are mapped to a low-dimensional space (encoding and decoding node attributes or sample features), our GSR-Net operates directly on a single connectome: a fully connected graph where conventionally, a node denotes a brain region, nodes have no features, and edge weights denote brain connectivity strength between two regions of interest (ROIs).在没有原始节点特征的情况下，我们最初为每个脑ROI（节点）分配了身份特征向量，然后利用学习的本地接收场来学习节点特征表示。其次，受光谱理论的启发，我们通过用图形超分辨率（GSR）层和两个图形卷积网络层将U-NET体系结构的对称性打破了对称性，以预测HR图，同时保留LR输入的特征。我们提出的GSR-NET框架的表现优于其变体，用于预测低分辨率连接组的高分辨率大脑功能连接。
Catchy but rigorous deep learning architectures were tailored for image super-resolution (SR), however, these fail to generalize to non-Euclidean data such as brain connectomes. Specifically, building generative models for super-resolving a low-resolution (LR) brain connectome at a higher resolution (HR) (i.e., adding new graph nodes/edges) remains unexplored although this would circumvent the need for costly data collection and manual labelling of anatomical brain regions (i.e. parcellation). To fill this gap, we introduce GSR-Net (Graph Super-Resolution Network), the first super-resolution framework operating on graph-structured data that generates high-resolution brain graphs from low-resolution graphs. First, we adopt a U-Net like architecture based on graph convolution, pooling and unpooling operations specific to non-Euclidean data. However, unlike conventional U-Nets where graph nodes represent samples and node features are mapped to a low-dimensional space (encoding and decoding node attributes or sample features), our GSR-Net operates directly on a single connectome: a fully connected graph where conventionally, a node denotes a brain region, nodes have no features, and edge weights denote brain connectivity strength between two regions of interest (ROIs). In the absence of original node features, we initially assign identity feature vectors to each brain ROI (node) and then leverage the learned local receptive fields to learn node feature representations. Second, inspired by spectral theory, we break the symmetry of the U-Net architecture by topping it up with a graph super-resolution (GSR) layer and two graph convolutional network layers to predict a HR graph while preserving the characteristics of the LR input. Our proposed GSR-Net framework outperformed its variants for predicting high-resolution brain functional connectomes from low-resolution connectomes.
