Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network
Journal:
arXiv
Published Date:
Apr 13, 2025
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.
