# BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology

Source: [BioX-CPath: Biologically-driven Explainable Diagnostics for Multistain IHC Computational Pathology](<https://www.sota2.com/research/paper/68fa5f761e209131ce9ea25e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-01-04T04:20:09.089Z

Authors: Amaya Gallagher-Syed, Henry Senior, Omnia Alwazzan, Elena Pontarini, Michele Bombardieri, Costantino Pitzalis, Myles J. Lewis, Michael R. Barnes, Luca Rossi, Gregory Slabaugh

Published: 2025-03-26T18:00:22.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Histopathology Image Classification on RA](<https://www.sota2.com/research/sota/histopathology-image-classification-on-ra.md>): Histopathology Image Classification; RA; Accuracy: 90.

- [Histopathology Image Classification on Sjogren](<https://www.sota2.com/research/sota/histopathology-image-classification-on-sjogren.md>): Histopathology Image Classification; Sjogren; Accuracy: 84.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.20880>)

[PDF](<https://arxiv.org/pdf/2503.20880.pdf>)

[Implementation](<https://github.com/AmayaGS/BioX-CPath>)