Core Concepts
MS-GCN enhances digital pathology models by integrating multiscale data, improving performance and interpretability.
Stats
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 ā a crucial aspect of learning from gigapixel H&E-stained whole slide images (WSI).
Through testing on different datasets, MS-GCN demonstrates superior performance over existing single-magnification GCN methods.