Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network

Inflammatory bowel disease (IBD) involves chronic inflammation of the digestive tract, with treatment options often burdened by adverse effects. Identifying biomarkers for personalized treatment is crucial. While immune cells play a key role in IBD, accurately identifying ulcer regions in whole slide images (WSIs) is essential for characterizing these cells and exploring potential therapeutics. Multiple instance learning (MIL) approaches have advanced WSI analysis but they lack spatial context awareness. In this work, we propose a weakly-supervised model called DomainGCN that employs a graph convolution neural network (GCN) and incorporates domain-specific knowledge of ulcer features, specifically, the presence of epithelium, lymphocytes, and debris for WSI-level ulcer prediction in IBD. We demonstrate that DomainGCN outperforms various state-of-the-art (SOTA) MIL methods and show the added value of domain knowledge.

@article{ding2025_2504.09430,
  title={ Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network },
  author={ Ruiwen Ding and Lin Li and Rajath Soans and Tosha Shah and Radha Krishnan and Marc Alexander Sze and Sasha Lukyanov and Yash Deshpande and Antong Chen },
  journal={arXiv preprint arXiv:2504.09430},
  year={ 2025 }
}