BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866908299826298880 |
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| author | Gallagher-Syed, Amaya Senior, Henry Alwazzan, Omnia Pontarini, Elena Bombardieri, Michele Pitzalis, Costantino Lewis, Myles J. Barnes, Michael R. Rossi, Luca Slabaugh, Gregory |
| author_facet | Gallagher-Syed, Amaya Senior, Henry Alwazzan, Omnia Pontarini, Elena Bombardieri, Michele Pitzalis, Costantino Lewis, Myles J. Barnes, Michael R. Rossi, Luca Slabaugh, Gregory |
| contents | The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explainable graph neural network architecture for whole slide image (WSI) classification that leverages both spatial and semantic features across multiple stains. At its core, BioX-CPath introduces a novel Stain-Aware Attention Pooling (SAAP) module that generates biologically meaningful, stain-aware patient embeddings. Our approach achieves state-of-the-art performance on both Rheumatoid Arthritis and Sjogren's Disease multistain datasets. Beyond performance metrics, BioX-CPath provides interpretable insights through stain attention scores, entropy measures, and stain interaction scores, that permit measuring model alignment with known pathological mechanisms. This biological grounding, combined with strong classification performance, makes BioX-CPath particularly suitable for clinical applications where interpretability is key. Source code and documentation can be found at: https://github.com/AmayaGS/BioX-CPath. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20880 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology Gallagher-Syed, Amaya Senior, Henry Alwazzan, Omnia Pontarini, Elena Bombardieri, Michele Pitzalis, Costantino Lewis, Myles J. Barnes, Michael R. Rossi, Luca Slabaugh, Gregory Computer Vision and Pattern Recognition Cell Behavior Quantitative Methods Tissues and Organs The development of biologically interpretable and explainable models remains a key challenge in computational pathology, particularly for multistain immunohistochemistry (IHC) analysis. We present BioX-CPath, an explainable graph neural network architecture for whole slide image (WSI) classification that leverages both spatial and semantic features across multiple stains. At its core, BioX-CPath introduces a novel Stain-Aware Attention Pooling (SAAP) module that generates biologically meaningful, stain-aware patient embeddings. Our approach achieves state-of-the-art performance on both Rheumatoid Arthritis and Sjogren's Disease multistain datasets. Beyond performance metrics, BioX-CPath provides interpretable insights through stain attention scores, entropy measures, and stain interaction scores, that permit measuring model alignment with known pathological mechanisms. This biological grounding, combined with strong classification performance, makes BioX-CPath particularly suitable for clinical applications where interpretability is key. Source code and documentation can be found at: https://github.com/AmayaGS/BioX-CPath. |
| title | BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology |
| topic | Computer Vision and Pattern Recognition Cell Behavior Quantitative Methods Tissues and Organs |
| url | https://arxiv.org/abs/2503.20880 |