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Endoscopic Procedures III: Video Capsule Endoscopy01:28

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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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Improving Foundation Model for Endoscopy Video Analysis via Representation Learning on Long Sequences.

Zhao Wang, Chang Liu, Lingting Zhu

    IEEE Journal of Biomedical and Health Informatics
    |March 3, 2025
    PubMed
    Summary

    A new foundation model, EndoFM-LV, effectively analyzes long endoscopy videos by learning from minute-level sequences. This approach significantly improves performance on classification, segmentation, detection, and workflow recognition tasks.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Current endoscopy video analysis often uses short clips, missing crucial spatial and temporal information from long streams.
    • Existing methods overlook the unique characteristics of continuous endoscopy data.

    Purpose of the Study:

    • To introduce EndoFM-LV, a foundation model pre-trained on minute-level long endoscopy video sequences.
    • To develop a novel masked token modeling scheme for self-supervised learning from extended video data.

    Main Methods:

    • Developed EndoFM-LV using a teacher-student framework for self-supervised pre-training on long videos.
    • Constructed a large dataset of 6,469 long endoscopy videos (>1 minute each, >13 million frames).
    • Evaluated EndoFM-LV on classification, segmentation, detection, and workflow recognition tasks.

    Main Results:

    • EndoFM-LV significantly outperformed prior state-of-the-art video- and frame-based methods.
    • Achieved superior results compared to Endo-FM and EndoSSL across all evaluated tasks.
    • Demonstrated substantial improvements in F1, Dice, and accuracy metrics.

    Conclusions:

    • EndoFM-LV effectively captures semantic spatial and temporal information from long endoscopy videos.
    • The minute-level pre-training framework enhances representation learning for endoscopy analysis.
    • This model offers a more robust and accurate approach to various endoscopic tasks.