An adaptive multi-graph fusion for tumor grading in pathology images
Islam Alzoubi, Bowen Xin, Rolf Bjerkvig, Jian Wang, Xiuying Wang · Pattern Recognition · 2025
Cancer grading is crucial for patient care, but integrating diverse knowledge of cancer pathogenesis in deep-learning models remains challenging. Traditional graph neural networks (GNN) often overlook the need to connect and balance the information across different levels of granularity, which is essential in histopathology for accurately capturing cellular morphology, tissue architecture, and spatial relationships. To address these challenges and provide a more holistic view of the patient's condition, we propose an Adaptive Multi-Graph Fusion-based Attentive Graph Neural Network (AMGF-GNN), which includes (1) three distinct graphs constructed and processed based on community information, feature similarity, and a combination of both to learn cell-to-cell interactions from three different views, enabling to distinguish the influence of each node in every single graph, (2) Adaptive Attentive Multi-Graph Fusion module which adaptively fuses the embeddings from all three paths, ensuring that the most relevant features from each graph are prioritized, and learns to balance the contributions of the community and feature-based information, addressing the uncertainty of their relative importance for the final grading task, (3) a dual-level loss optimization incorporates intra-graph and inter-graph similarity measures, ensuring consistency and robustness in the learned embeddings. The proposed method was experimentally validated and compared with eight other state-of-the-art (SOTA) models using the glioma TCGA and invasive ductal carcinoma (IDC) datasets. It achieved a superior accuracy of 89.68% for the binary grading of the glioma TCGA dataset, outperforming other competing models and demonstrating the effectiveness of AMGF-GNN.