在结肠镜检查中,用于人工智能辅助的多体检测的最佳标签方法
Yen-Po Wang1, Ying-Chun Jheng2, Ming-Chih Hou3
1Endoscopy Center for Diagnosis and Treatment, Taipei Veterans General Hospital, Taiwan; Division of Gastroenterology, Taipei Veterans General Hospital, Taiwan; Institute of Brain Science, National Yang Ming Chiao Tung University School of Medicine, Taiwan; Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taiwan.
标准化结肠多标签对于准确的AI模型至关重要. 在注释中将边界框延长20%显著提高了AI多胞体检测准确性和性能.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 用于机器学习的结肠多标签缺乏标准化的方法.
- 开发精确的人工智能模型来检测多胞体需要优化注释技术.
研究的目的:
- 确定最佳的结肠多注释方法,以提高AI模型的准确性.
- 为了比较不同的边界框扩展百分比与人工智能培训的精确细分.
主要方法:
- 用了3542张结肠镜图像进行手动的多胞体注释.
- 对比精确的轮细分与长方形框扩展10%至50%.
- 开发了一个U-Net卷积神经网络模型,用于自动细分.
主要成果:
- 将边界框延长20%产生了最佳性能:95.42%的精度,94.84%的灵敏度,95.41%的F1-score.
- 精确的轮细分实现了99.6%的灵敏度,但只有77.47%的精度.
- 20%扩展的界限框模型表现出卓越的性能,AUC为0.971.
结论:
- 注释方法直接影响结肠多检测中的AI模型可预测性.
- 20%的边界框扩展提供了最准确的AI预测模型.
- 标准化结肠多标签协议对于比较AI模型精度至关重要.
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