Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images

In pathology, cancer grading is crucial for patient management and treatment. Recent deep learning methods, based upon convolutional neural networks (CNNs), have shown great potential for automated and accurate cancer diagnosis. However, these do not explicitly utilize tissue/cellular composition, and thus difficult to incorporate the existing knowledge of cancer pathology. In this study, we propose a multi-cell type and multi-level graph aggregation network (MMGA-Net) for cancer grading. Given a pathology image, MMGA-Net constructs multiple cell graphs at multiple levels to represent intra- and inter-cell type relationships and to incorporate global and local cell-to-cell interactions. In addition, it extracts tissue contextual information using a CNN. Then, the tissue and cellular information are fused to predict a cancer grade. The experimental results on two types of cancer datasets demonstrate the effectiveness of MMGA-Net, outperforming other competing models. The results also suggest that the information fusion of multiple cell types and multiple levels via graphs is critical for improved pathology image analysis. •Propose a novel graph-based fusion model for cancer grading in pathology images.•Propose a multi-cell type and multi-level graph aggregation for pathology images.•Explore intra- and inter-cell type relationships in multi-cell type graphs.•Exploit global and local cell-to-cell interactions in multi-level graphs.•Outperform SOTA CNN- and GNN-based models on multi-organ cancer datasets.