Abstract：
Graph convolutional neural networks have shown significant potential in natural and histopathology images. However, their use has only been studied in a single magnification or multi-magnification with either homogeneous graphs or only different node types. In order to leverage the multi-magnification information and improve message passing with graph convolutional networks, we handle different embedding spaces at each magnification by introducing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN) as a multiple instance learning method. We model histopathology image patches and their relation with neighboring patches and patches at other scales (i.e., magnifications) as a graph. We define separate message-passing neural networks based on node and edge types to pass the information between different magnification embedding spaces. We experiment on prostate cancer histopathology images to predict the grade groups based on the extracted features from patches. We also compare our MS-RGCN with multiple state-of-the-art methods with evaluations on several source and held-out datasets. Our method outperforms the state-of-the-art on all of the datasets and image types consisting of tissue microarrays, whole-mount slide regions, and whole-slide images. Through an ablation study, we test and show the value of the pertinent design features of the MS-RGCN.
摘要：
图卷积神经网络在自然和组织病理学图像中显示出巨大的潜力。然而,它们的使用只在单放大或多放大与均匀的图形或仅不同的节点类型进行了研究。为了利用多放大倍数信息并改善图卷积网络的消息传递，我们通过引入多尺度关系图卷积网络（MS-RGCN）作为多实例学习方法，在每次放大处理不同的嵌入空间。我们对组织病理学图像斑块及其与相邻斑块和其他尺度上的斑块的关系进行建模(即，放大倍数)作为图形。我们根据节点和边缘类型定义单独的消息传递神经网络，以在不同的放大嵌入空间之间传递信息。我们对前列腺癌组织病理学图像进行实验，以根据从补丁中提取的特征来预测等级组。我们还将MS-RGCN与多种最新方法进行了比较，并对多个来源和保留数据集进行了评估。我们的方法在由组织微阵列组成的所有数据集和图像类型上都优于最先进的技术，整个安装幻灯片区域，和整个幻灯片图像。通过消融研究,我们测试并显示MS-RGCN的相关设计功能的价值。
