BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
This work addresses the need for interpretable diagnostics in clinical pathology, particularly for multistain IHC, though it appears incremental as it builds on existing graph neural network methods with a novel attention module.
The paper tackled the challenge of developing biologically interpretable models for multistain immunohistochemistry analysis in computational pathology by introducing BioX-CPath, an explainable graph neural network architecture that achieved state-of-the-art performance on Rheumatoid Arthritis and Sjogren's Disease datasets.
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.