用CLIP模型自动评估肠镜清洁度，减少人工主观误差。
CLIP Based Region-Aware Feature Fusion for Automated BBPS Scoring in Colonoscopy Images
- 结合文本先验与视觉特征融合，无需分割粪便
- 在2240张图像上实现比基线更高准确率
- 适合临床辅助肠镜分析系统开发
准确评估肠道清洁度对结肠镜检查至关重要。波士顿肠道准备评分（BBPS）虽为标准化评分体系，但人工评分存在主观性与观察者间差异。本文构建了一个高质量结肠镜数据集，包含来自517名受试者的2,240张图像，均由专家达成一致的BBPS评分标注。提出一种基于CLIP的自动化BBPS评分框架，采用适配器式迁移学习与专门的粪便特征提取分支，融合全局视觉特征与粪便相关文本先验，提升肠道清洁度评估精度，无需显式分割。在自建数据集与公开的NERTHU数据集上的大量实验表明，该方法优于现有基线，具备临床辅助结肠镜分析部署潜力。
原文摘要 · Abstract (English)
Accurate assessment of bowel cleanliness is essential for effective colonoscopy procedures. The Boston Bowel Preparation Scale (BBPS) offers a standardized scoring system but suffers from subjectivity and inter-observer variability when performed manually. In this paper, to support robust training and evaluation, we construct a high-quality colonoscopy dataset comprising 2,240 images from 517 subjects, annotated with expert-agreed BBPS scores. We propose a novel automated BBPS scoring framework that leverages the CLIP model with adapter-based transfer learning and a dedicated fecal-feature extraction branch. Our method fuses global visual features with stool-related textual priors to improve the accuracy of bowel cleanliness evaluation without requiring explicit segmentation. Extensive experiments on both our dataset and the public NERTHU dataset demonstrate the superiority of our approach over existing baselines, highlighting its potential for clinical deployment in computer-aided colonoscopy analysis.
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