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Related Experiment Video

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Leveraging large language and vision models for knowledge extraction from large-scale image-text colonoscopy records.

Shuo Wang1,2,3,4, Yan Zhu5,6, Zhiwei Yang7,8,9

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Summary

This study introduces EndoKED, an AI approach that automatically extracts detailed polyp information from colonoscopy records. This method significantly improves polyp detection and segmentation for better artificial intelligence in gastroenterology.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • AI for colonoscopy analysis requires large, diverse, expert-annotated datasets, which are often limited.
  • Routine clinical colonoscopy records offer vast image-text data but are labor-intensive to annotate.
  • Existing methods struggle with dataset limitations, hindering AI model performance and generalization.

Purpose of the Study:

  • To develop an automated data mining paradigm, EndoKED, for deep knowledge extraction from colonoscopy records.
  • To transform raw colonoscopy image-text data into pixel-level annotated datasets for AI training.
  • To enhance polyp detection, segmentation, and optical biopsy capabilities using AI.

Main Methods:

  • Leveraging advancements in large language and vision models.
  • Proposing EndoKED, a data mining paradigm for knowledge extraction and distillation.
  • Automating the creation of pixel-level annotated image datasets from raw colonoscopy records.

Main Results:

  • EndoKED demonstrated superior performance in detecting polyps at both report and image levels.
  • Pixel-level polyp annotation was achieved, significantly improving segmentation models.
  • Pretraining with EndoKED led to state-of-the-art performance and generalization in polyp segmentation.
  • The EndoKED vision backbone enabled expert-level performance in optical biopsy with data-efficient learning.

Conclusions:

  • EndoKED effectively automates the creation of annotated datasets from clinical colonoscopy records.
  • The proposed method significantly advances AI capabilities in polyp detection, segmentation, and optical biopsy.
  • EndoKED facilitates the development of more robust and generalizable AI tools for colonoscopy analysis.