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.

翻译：图卷积网络（GCNs）已成为数字病理学中替代卷积神经网络多实例学习的强大方法，能够更好地处理跨不同空间范围的结构信息——这是从千兆像素H&E染色全切片图像（WSI）中学习的关键方面。然而，图消息传递算法在聚合大邻域时常遭遇过度平滑问题。因此，有效建模多范围交互依赖对图的精心构建提出要求。我们提出的多尺度GCN（MS-GCN）通过利用WSI中多个放大倍率层的信息解决了这一难题。MS-GCN能够在低倍率下同步建模长程结构依赖性，在高倍率下捕捉高分辨率细胞细节，类似于病理学家通常执行的分析流程。该架构的独特配置允许同时建模低倍率下的结构模式和高倍率下的精细细胞特征，同时量化每个放大倍率对预测结果的贡献。通过在不同数据集上的测试，MS-GCN展现出优于现有单倍率GCN方法的性能。本方法带来的性能提升与可解释性增强，有望推动计算病理学模型的进步，尤其在需要广泛空间背景信息的任务中。