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

Abstract

Objective: Ulcerative colitis (UC), characterized by chronic inflammation with alternating remission-relapse cycles, requires precise histological healing (HH) evaluation to improve clinical outcomes. To overcome 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 integrating white light endoscopy (WLE) and endocytoscopy (EC). Methods:VIGIL begins with a dual-branch MIL module KS-MIL based on top-K typical frames selection and similarity metric adaptive learning to learn relationships among frame features effectively. By integrating the diagnostic report text and specially designed multi-level alignment and supervision between image-text pairs, VIGIL establishes joint image-text guidance during training to capture richer disease-related semantic information. Furthermore, VIGIL employs a multi-modal masked relation fusion (MMRF) strategy to uncover the latent diagnostic correlations of two endoscopic image 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 successfully 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.

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Reproductions