在数字病理学中利用金字塔图卷积网络整合多尺度拓扑结构
Integrating multiscale topology in digital pathology with pyramidal graph convolutional networks
AI总结：
提出多尺度图卷积网络MS-GCN，通过整合WSI多放大倍数信息同时建模长程结构与细胞细节，克服过度平滑问题，性能与可解释性优于单倍数方法。
摘要：
图卷积网络（GCN）已成为数字病理学中基于卷积神经网络的多示例学习的强大替代方案，能够更好地处理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较大邻域时常常面临过度平滑的问题。因此，对多范围交互的有效建模依赖于图的精细构建。我们提出的多尺度GCN（MS-GCN）通过利用WSI中多个放大倍数级别的信息来解决这一问题。MS-GCN能够同时建模低倍镜下的长程结构依赖和高倍镜下的高分辨率细胞细节，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在低倍镜下并发建模结构模式，并在高倍镜下处理详细的细胞特征，同时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展现出优于现有单一放大倍数GCN方法的性能。该方法在性能和可解释性方面的提升，为推动计算病理学模型的发展带来了希望，尤其是在需要广泛空间背景的任务中。
英文摘要：
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.
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