Multi-Scale Relational Graph Convolutional Network for Multiple Instance Learning in Histopathology Images
This work addresses the challenge of early fusion in graph-based multiple instance learning for histopathology image analysis, offering improvements in cancer grading accuracy.
The authors tackled the problem of leveraging multi-magnification information in histopathology images for cancer grading by proposing the Multi-Scale Relational Graph Convolutional Network (MS-RGCN), which outperformed state-of-the-art methods on all evaluated datasets and image types.
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 late fusion. In order to leverage the multi-magnification information and early fusion 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. To pass the information between different magnification embedding spaces, we define separate message-passing neural networks based on the node and edge type. 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.