DualGCN-MIL: Whole Slide Image Classification Based on Double Relationship Graph Learning

The resolution of a whole slide image (WSI) is too large to process directly, but WSI can be segmented into patches and be classified through multiple instance learning (MIL). Some patches have either close distances or similar pathological morphology, indicating that there are at least two types of relationships between patches. However, the existing MIL methods often deal with instance relationships simplistically. To solve this problem, we propose a new model named DualGCN-MIL. It analyzes the two types of relationships in WSI by constructing two different graphs in high-dimensional feature space and physical coordinate space and uses graph convolutional networks with unshared parameters for learning. Various experiments conducted on the Camelyon dataset have shown that compared to state-of-the-art methods, we can achieve better performance in multiple indicators, with accuracy of 88.89% and 85.11% on the 16 and 17 datasets, respectively, which proves that DualGCN-MIL has a higher accuracy in WSI classification. Implementation can be obtained from https://github.com/UnmatchedKatana/DualGCN-MIL.