VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction.

OBJECTIVE: Ulcerative colitis (UC) is characterized by chronic inflammation with alternating remission-relapse cycles, necessitating precise histological healing (HH) evaluation to improve clinical outcomes. To address the limitations of annotation-intensive deep learning methods and suboptimal multi-instance learning (MIL) in HH prediction, we propose VIGIL, the first vision-language-guided MIL framework that integrates white-light endoscopy (WLE) and endocytoscopy (EC). METHODS: VIGIL introduces a dual-branch MIL module, KS-MIL, based on top-K typical frame selection and similarity-weighted metric learning to effectively model relationships among frame features. By incorporating diagnostic report text and a specially designed multi-level alignment and supervision strategy between image-text pairs, VIGIL establishes joint vision-language guidance during training to capture richer disease-related semantic information. Furthermore, VIGIL employs a multi-modal masked relation fusion (MMRF) strategy to uncover latent diagnostic correlations between WLE and EC representations. RESULTS: Comprehensive experiments on a real-world clinical dataset demonstrate VIGIL's superior performance, achieving 92.69% accuracy and 94.79% AUC, outperforming existing state-of-the-art methods. CONCLUSION: The proposed VIGIL framework establishes an effective vision-language guided MIL paradigm for UC HH prediction, reducing annotation burdens while improving prediction reliability. SIGNIFICANCE: The research outcomes provide new insights for non-invasive UC diagnosis and hold theoretical significance and clinical value for advancing intelligent healthcare development.