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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.02542 |
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| _version_ | 1866912411150188544 |
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| author | Kormann, Niklas Ramuz, Masoud Nisar, Zeeshan Schaadt, Nadine S. Annuth, Hendrik Doerr, Benjamin Feuerhake, Friedrich Lampert, Thomas Lutzeyer, Johannes F. |
| author_facet | Kormann, Niklas Ramuz, Masoud Nisar, Zeeshan Schaadt, Nadine S. Annuth, Hendrik Doerr, Benjamin Feuerhake, Friedrich Lampert, Thomas Lutzeyer, Johannes F. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification Kormann, Niklas Ramuz, Masoud Nisar, Zeeshan Schaadt, Nadine S. Annuth, Hendrik Doerr, Benjamin Feuerhake, Friedrich Lampert, Thomas Lutzeyer, Johannes F. Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Quantitative Methods 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. |
| title | HIEGNet: A Heterogenous Graph Neural Network Including the Immune Environment in Glomeruli Classification |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2506.02542 |