BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
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.
