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arXiv · 2504.09430

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

Ruiwen Ding·Lin Li·Rajath Soans·Tosha Shah·Radha Krishnan·Marc Alexander Sze·Sasha Lukyanov·Yash Deshpande·Antong Chen
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Abstract

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.

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Ruiwen Ding, Lin Li, Rajath Soans, Tosha Shah, Radha Krishnan, Marc Alexander Sze, Sasha Lukyanov, Yash Deshpande, Antong Chen. 2025-04-13. Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network. https://arxiv.org/abs/2504.09430

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