改进基于人工智能的狗心脏病诊断,使用专家共识的听觉标签
概括
减少狗心脏声AI模型中的标签噪声显著提高了准确性. 结合多个专家意见和利用个体心脏周期,提高了检测神经膜病的分类性能.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 噪音标签在兽医诊断中对人工智能模型培训提出了重大挑战.
- 专家评估犬耳听数据的模糊性可能会对分类性能产生负面影响.
- 密克索马托斯中枢病 (MMVD) 是狗常见的心脏病,通常使用听力诊断来诊断.
研究的目的:
- 检查犬类心脏声音记录 (HSR) 中的专家评估含糊性.
- 评估标签噪声对MMVDAI分类性能的影响.
- 通过多个专家意见,引入和评估标签降噪方法.
主要方法:
- 专家对140个HSR的数据集进行了标注,以测量全盘心脏声强度.
- 用多个专家意见来选择70个高质量的HSR,创建一个降噪的数据集.
- 三种分类算法 (AdaBoost,XGBoost,Random Forest) 在原始和降噪数据集上进行训练和评估,并利用单个心脏周期来扩展数据.
主要成果:
- 标签噪声降低显著提高了所有测试算法的分类准确性,XGBoost显示了最显著的收益.
- 对于轻度心脏声,灵敏度从37.71%增加到90.98%,特异性从76.70%增加到93.69%.
- 在中度 (灵敏度30.23%至55.81%,特异性64.56%至97.19%) 和大声/激动人心的声 (灵敏度58.28%至95.09%,特异性84.84%至89.69%) 中也观察到改善.
结论:
- 尽量减少标签噪声对于提高兽医心脏病学AI分类算法的性能至关重要.
- 结合多个专家意见有效地减少了狗心脏声数据集中的标签噪声.
- 通过利用个人心脏周期以及降噪策略,提高了MMVD检测的分类稳定性.
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