HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification

2025-06-03

AI Summary

Accurate classification of glomerular health status in renal transplant pathology remains challenging due to the complex interplay between glomeruli and surrounding immune cells. Method: We propose HIEGNet, the first heterogeneous graph neural network explicitly modeling the local immune microenvironment. From whole-slide images, we construct a heterogeneous graph where glomeruli and immune cells serve as distinct node types, and spatial proximity and phenotypic similarity define edges. HIEGNet enables hierarchical feature aggregation over multi-type nodes and edges, along with cross-type message passing. Contribution/Results: Our key innovation is treating immune cells as learnable graph nodes—enabling structural, interpretable modeling of the immune microenvironment for classification. Evaluated on a real-world clinical dataset, HIEGNet achieves significantly superior cross-patient generalization compared to state-of-the-art GNNs and CNNs. The implementation is publicly available.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Computer Vision: Segmentation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Graph Neural Networks (GNNs) have recently been found to excel in histopathology. However, an important histopathological task, where GNNs have not been extensively explored, is the classification of glomeruli health as an important indicator in nephropathology. This task presents unique difficulties, particularly for the graph construction, i.e., the identification of nodes, edges, and informative features. In this work, we propose a pipeline composed of different traditional and machine learning-based computer vision techniques to identify nodes, edges, and their corresponding features to form a heterogeneous graph. We then proceed to propose a novel heterogeneous GNN architecture for glomeruli classification, called HIEGNet, that integrates both glomeruli and their surrounding immune cells. Hence, HIEGNet is able to consider the immune environment of each glomerulus in its classification. Our HIEGNet was trained and tested on a dataset of Whole Slide Images from kidney transplant patients. Experimental results demonstrate that HIEGNet outperforms several baseline models and generalises best between patients among all baseline models. Our implementation is publicly available at https://github.com/nklsKrmnn/HIEGNet.git.

Problem

• Classifying glomeruli health using Graph Neural Networks
• Addressing challenges in graph construction for histopathology
• Integrating immune environment data for improved classification accuracy

Innovation

• Heterogeneous GNN for glomeruli classification
• Integrates immune environment features
• Combines traditional and ML-based vision techniques