Spatially-constrained and -unconstrained bi-graph interaction network for multi-organ pathology image classification

• Published in: IEEE transactions on medical imaging Vol. PP; p. 1
• Main Authors: Bui, Doanh C., Song, Boram, Kim, Kyungeun, Kwak, Jin Tae
• Format: Journal Article
• Language: English
• Published: United States IEEE 31.07.2024
• Subjects: Cancer; cancer classification; Computational modeling; Computational pathology; graph neural network; Image classification; Image edge detection; interaction; Pathology; Transformer; Transformers; Vectors
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2024.3436080

In computational pathology, graphs have shown to be promising for pathology image analysis. There exist various graph structures that can discover differing features of pathology images. However, the combination and interaction between differing graph structures have not been fully studied and utilized for pathology image analysis. In this study, we propose a parallel, bi-graph neural network, designated as SCUBa-Net, equipped with both graph convolutional networks and Transformers, that processes a pathology image as two distinct graphs, including a spatially-constrained graph and a spatially-unconstrained graph. For efficient and effective graph learning, we introduce two inter-graph interaction blocks and an intra-graph interaction block. The inter-graph interaction blocks learn the node-to-node interactions within each graph. The intra-graph interaction block learns the graph-to-graph interactions at both global- and local-levels with the help of the virtual nodes that collect and summarize the information from the entire graphs. SCUBa-Net is systematically evaluated on four multi-organ datasets, including colorectal, prostate, gastric, and bladder cancers. The experimental results demonstrate the effectiveness of SCUBa-Net in comparison to the state-of-the-art convolutional neural networks, Transformer, and graph neural networks.