Graph convolutional networks (GCNs) have emerged as a powerful alternative to
multiple instance learning with convolutional neural networks in digital
pathology, offering superior handling of structural information across various
spatial ranges - a crucial aspect of learning from gigapixel H&E-stained whole
slide images (WSI). However, graph message-passing algorithms often suffer from
oversmoothing when aggregating a large neighborhood. Hence, effective modeling
of multi-range interactions relies on the careful construction of the graph.
Our proposed multi-scale GCN (MS-GCN) tackles this issue by leveraging
information across multiple magnification levels in WSIs. MS-GCN enables the
simultaneous modeling of long-range structural dependencies at lower
magnifications and high-resolution cellular details at higher magnifications,
akin to analysis pipelines usually conducted by pathologists. The
architecture's unique configuration allows for the concurrent modeling of
structural patterns at lower magnifications and detailed cellular features at
higher ones, while also quantifying the contribution of each magnification
level to the prediction. Through testing on different datasets, MS-GCN
demonstrates superior performance over existing single-magnification GCN
methods. The enhancement in performance and interpretability afforded by our
method holds promise for advancing computational pathology models, especially
in tasks requiring extensive spatial context.

图卷积网络（GCNs）在数字病理学中作为卷积神经网络的多实例学习强大替代品，能够优秀地处理各种空间范围内的结构信息，对于从吉格巴 H&E 染色全幻灯片图像（WSI）中学习至关重要。然而，图传递算法在聚合大邻域时往往存在过度平滑的问题，因此，多尺度 GCN（MS-GCN）通过利用 WSIs 中多个放大级别上的信息解决了这个问题。MS-GCN 能够同时对较低放大倍数下的长程结构依赖性和较高放大倍数下的高分辨率细胞细节进行建模，类似于病理学家通常进行的分析流程。该架构的独特配置允许同时对较低放大倍数下的结构模式和较高放大倍数下的细胞特征进行建模，并量化每个放大倍数级别对预测的贡献。通过在不同数据集上的测试，MS-GCN 在现有单放大倍数 GCN 方法上展现出优越的性能。我们的方法在性能和可解释性方面的提升有望推动计算病理学模型的发展，特别是在需要广泛空间背景的任务中。