Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images
Abstract In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of analyzing WSIs is how information across tiles is aggre...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-99042-4 |
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| author | Ruiwen Ding Kha-Dinh Luong Erika Rodriguez Ana Cristina Araujo Lemos da Silva William Hsu |
| author_facet | Ruiwen Ding Kha-Dinh Luong Erika Rodriguez Ana Cristina Araujo Lemos da Silva William Hsu |
| author_sort | Ruiwen Ding |
| collection | DOAJ |
| description | Abstract In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of analyzing WSIs is how information across tiles is aggregated to predict outcomes such as patient prognosis. We introduce a model that combines a message-passing graph neural network (GNN) with a state space model (Mamba) to capture both local and global spatial relationships among the tiles in WSIs. The model’s effectiveness was demonstrated in predicting progression-free survival among patients with early-stage lung adenocarcinomas (LUAD). We compared the model with other state-of-the-art methods for tile-level information aggregation in WSIs, including statistics-based, multiple instance learning (MIL)-based, GNN-based, and GNN-transformer-based aggregation. Our model achieved the highest c-index (0.70) and has the largest number of parameters among comparison models yet maintained a short inference time. Additional experiments showed the impact of different types of node features and different tile sampling strategies on model performance. Code: https://github.com/rina-ding/gat-mamba . |
| format | Article |
| id | doaj-art-4a299b9c266b411ab83fe861fb8b87ea |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4a299b9c266b411ab83fe861fb8b87ea2025-08-20T03:22:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-99042-4Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide imagesRuiwen Ding0Kha-Dinh Luong1Erika Rodriguez2Ana Cristina Araujo Lemos da Silva3William Hsu4Medical and Imaging Informatics, Department of Radiological Sciences, Department of Bioengineering, University of CaliforniaDepartment of Computer Science, University of California, Santa BarbaraDepartment of Pathology and Laboratory Sciences, University of CaliforniaDepartment of Pathology, Federal University of UberlandiaMedical and Imaging Informatics, Department of Radiological Sciences, Department of Bioengineering, University of CaliforniaAbstract In computational pathology, extracting and representing spatial features from gigapixel whole slide images (WSIs) are fundamental tasks, but due to their large size, WSIs are typically segmented into smaller tiles. A critical aspect of analyzing WSIs is how information across tiles is aggregated to predict outcomes such as patient prognosis. We introduce a model that combines a message-passing graph neural network (GNN) with a state space model (Mamba) to capture both local and global spatial relationships among the tiles in WSIs. The model’s effectiveness was demonstrated in predicting progression-free survival among patients with early-stage lung adenocarcinomas (LUAD). We compared the model with other state-of-the-art methods for tile-level information aggregation in WSIs, including statistics-based, multiple instance learning (MIL)-based, GNN-based, and GNN-transformer-based aggregation. Our model achieved the highest c-index (0.70) and has the largest number of parameters among comparison models yet maintained a short inference time. Additional experiments showed the impact of different types of node features and different tile sampling strategies on model performance. Code: https://github.com/rina-ding/gat-mamba .https://doi.org/10.1038/s41598-025-99042-4 |
| spellingShingle | Ruiwen Ding Kha-Dinh Luong Erika Rodriguez Ana Cristina Araujo Lemos da Silva William Hsu Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images Scientific Reports |
| title | Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images |
| title_full | Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images |
| title_fullStr | Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images |
| title_full_unstemmed | Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images |
| title_short | Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images |
| title_sort | combining graph neural network and mamba to capture local and global tissue spatial relationships in whole slide images |
| url | https://doi.org/10.1038/s41598-025-99042-4 |
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