IEEE transactions on bio-medical engineering · 2026
VIGIL: Vision-Language Guided Multiple Instance Learning Framework for Ulcerative Colitis Histological Healing Prediction.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Qiu Zhengxuan, Peng Bo, Zhang Lingrui, Tang Xiaoying, Wang Jiankun, Guo Qin
Аннотация
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.
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.