Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models

2026-04-26

AI Summary

This study addresses the limitations of manual Nancy Index scoring for histological activity in ulcerative colitis—namely, its time intensity and high inter-observer variability—and overcomes the reliance of existing computational pathology methods on dense annotations that hinder generalizability. The authors propose a weakly supervised multiple instance learning framework that leverages only case- or slide-level Nancy labels to model whole-slide H&E images using foundation models such as Virchow2. For the first time, this approach enables fully automated five-grade Nancy Index scoring across multi-center real-world data from three hospitals. Experiments demonstrate that the Virchow2 encoder yields optimal performance, and the proposed simple ensemble strategy outperforms hierarchical gating baselines, highlighting the critical impact of foundation model selection and resolution on predictive accuracy. The method achieves robust and interpretable assessment of neutrophil activity.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Humans and AI: Other Foundations of Human Computation & AI
• Computer Vision: Other Foundations of Computer Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Histologic assessment of ulcerative colitis (UC) activity is an important endpoint in clinical trials and routine care, but manual grading with indices such as the Nancy histological index (NHI) is time-consuming and prone to observer variability. While computational pathology methods can automate scoring, many approaches depend on dense region-level annotations, which are costly to obtain, particularly in heterogeneous, multicenter cohorts. We propose a weakly supervised multiple instance learning (MIL) approach for whole-slide images that learns from case- and slide-level NHI labels, leveraging foundation models. Our method targets clinically relevant endpoints, including neutrophilic activity and derived Nancy-low/high groupings, enabling full five-grade NHI prediction. On a multicenter dataset of H&E-stained colon biopsies from three hospitals (2019-2025), we evaluate multiple foundation model encoders and aggregation strategies. We find that foundation model choice and resolution substantially affect performance, with Virchow2 providing the most consistent gains, and that a simple ensembling rule improves five-grade NHI prediction compared to a hierarchical gating baseline. Overall, our results demonstrate that weakly supervised MIL with modern foundation-model representations can provide robust, interpretable UC histology activity assessment in realistic multicenter settings.

Problem

• ulcerative colitis
• Nancy histological index
• weakly supervised learning
• multicenter
• computational pathology

Innovation

• weakly supervised learning
• foundation models
• multiple instance learning
• Nancy histological index
• computational pathology