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，它们的使用仅在同质图或仅不同节点类型的单倍放大或多倍放大中进行了研究。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来处理每个放大倍数下的不同嵌入空间。我们将组织病理学图像斑块及其与相邻斑块和其他尺度（即放大倍数）的斑块的关系建模为图表。我们根据节点和边类型定义单独的消息传递神经网络，以在不同放大倍数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，以根据从斑块中提取的特征来预测分级组。我们还将 MS-RGCN 与多种最先进的方法进行比较，并对多个源和保留的数据集进行评估。我们的方法在由组织微阵列、整体载玻片区域和整体载玻片图像组成的所有数据集和图像类型上均优于最先进的方法。通过消融研究，我们测试并展示了 MS-RGCN 相关设计特征的价值。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

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.Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.