在图像分析中对机器学习模型性能进行比较分析:数据集多样性和大小的影响
Eric D Pelletier1, Sean D Jeffries1,2, Kevin Song2
1From the Department of Experimental Surgery, McGill University Health Center, Montreal, Quebec, Canada.
Anesthesia and analgesia
|August 8, 2024
概括
数据集平衡是医学成像中的机器学习的关键. 一个多样化但足够大的数据集,如Multi-25 × 10,显著提高视频喉科镜分析性能.
科学领域:
- 医疗图像分析 医学图像分析
- 临床人工智能 临床人工智能
背景情况:
- 图像分析中的机器学习模型性能对于临床AI至关重要.
- 视频鼻镜手术对人工智能开发提出了独特的挑战.
研究的目的:
- 分析机器学习模型在视频鼻镜图像分析中的性能.
- 调查数据集多样性和大小对模型准确性的影响.
主要方法:
- 来自YouTube的377个视频喉科视频被用于创建6个数据集.
- 数据集因患者多样性和图像数量而异;应用了数据增强.
- 使用F1得分,精度,回忆和mAP指标评估了YOLOv5-Small和YOLOv8-Small模型.
主要成果:
- 数据集配置显著影响模型性能,多样性和数量的平衡是最佳的.
- 多25×10数据集 (25张图像,10名患者) 显示出优异的结果.
- 数据增强效应因数据集特征而异.
结论:
- 数据集结构对于医疗图像分析中的机器学习模型性能至关重要.
- 为了有效开发人工智能,数据集大小和多样性之间的最佳平衡是必要的.
- 这项研究突出了临床环境中数据驱动机器学习的复杂性.
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