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.
@article{arxiv.2503.20880,
title = {BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology},
author = {Amaya Gallagher-Syed and Henry Senior and Omnia Alwazzan and Elena Pontarini and Michele Bombardieri and Costantino Pitzalis and Myles J. Lewis and Michael R. Barnes and Luca Rossi and Gregory Slabaugh},
journal= {arXiv preprint arXiv:2503.20880},
year = {2025}
}