在计算病理学中，图表已被证明在病理图像分析中很有前景。存在多种可以发现病理图像的不同特征的图结构。然而，不同图结构之间的组合和相互作用尚未得到充分研究和用于病理图像分析。在这项研究中，我们提出了一种并行的双图神经网络，称为 SCUBa-Net，配备图卷积网络和 Transformer，将病理图像处理为两个不同的图，包括空间约束图和空间约束图。无约束图。为了高效且有效的图学习，我们引入了两个图间交互块和一个图内交互块。图间交互块学习每个图中的节点到节点的交互。图内交互块在收集和总结整个图中信息的虚拟节点的帮助下学习全局和局部级别的图到图交互。 SCUBa-Net 在四个多器官数据集上进行了系统评估，包括结直肠癌、前列腺癌、胃癌和膀胱癌。实验结果证明了 SCUBa-Net 与最先进的卷积神经网络、Transformer 和图神经网络相比的有效性。

In computational pathology, graphs have shown to be promising for pathology image analysis. There exist various graph structures that can discover differing features of pathology images. However, the combination and interaction between differing graph structures have not been fully studied and utilized for pathology image analysis. In this study, we propose a parallel, bi-graph neural network, designated as SCUBa-Net, equipped with both graph convolutional networks and Transformers, that processes a pathology image as two distinct graphs, including a spatially-constrained graph and a spatially-unconstrained graph. For efficient and effective graph learning, we introduce two inter-graph interaction blocks and an intra-graph interaction block. The inter-graph interaction blocks learn the node-to-node interactions within each graph. The intra-graph interaction block learns the graph-to-graph interactions at both global- and local-levels with the help of the virtual nodes that collect and summarize the information from the entire graphs. SCUBa-Net is systematically evaluated on four multi-organ datasets, including colorectal, prostate, gastric, and bladder cancers. The experimental results demonstrate the effectiveness of SCUBa-Net in comparison to the state-of-the-art convolutional neural networks, Transformer, and graph neural networks.