用领域知识增强图神经网络，精准识别炎症性肠病病理切片中的溃疡区域。
Predicting ulcer in H&E images of inflammatory bowel disease using domain-knowledge-driven graph neural network
- 基于图卷积网络结合溃疡特征的领域知识进行弱监督建模。
- 在IBD全切片图像上达到优于现有最先进方法的溃疡预测性能。
- 适合病理图像分析、医学影像智能诊断方向的研究者参考。
炎症性肠病（IBD）表现为消化道慢性炎症，治疗常伴随不良反应。寻找个性化治疗的生物标志物至关重要。虽然免疫细胞在IBD中起关键作用，但准确识别全切片图像（WSIs）中的溃疡区域，是表征这些细胞并探索潜在疗法的基础。现有多重实例学习（MIL）方法虽推动了WSI分析，但缺乏空间上下文感知能力。本文提出一种弱监督模型DomainGCN，采用图卷积神经网络（GCN），融合溃疡特征的领域知识——上皮缺失、淋巴细胞聚集和坏死碎片——实现IBD WSI级别的溃疡预测。实验表明，DomainGCN优于多种现有SOTA MIL方法，并验证了领域知识的附加价值。
原文摘要 · Abstract (English)
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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