基于深度学习的多重体检测性能变化的来源
概括
通常用于结肠癌查的验证指标缺乏临床相关性,并且显示出很高的可变性. 计算机视觉中的标准超参数不能确保临床上可信的结果,因此需要新的验证策略来对结肠镜中的深度学习进行验证.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 胃肠病学 胃肠病学
背景情况:
- 可靠的验证指标对于科学进步和医学成像方法的临床翻译至关重要.
- 现有的理论框架用于度量陷缺乏在特定应用中的实验验证.
- 使用深度学习进行结肠癌查需要强大的评估策略.
研究的目的:
- 解决关于结肠癌查验证验证指标陷的实验证据的差距.
- 介绍结肠癌检测内镜计算机视觉挑战的获奖解决方案.
- 展示常见指标对超参数的敏感性和不良指标选择的影响.
主要方法:
- 分析了来自六个临床中心的患者数据.
- 评估常用的物体检测指标.
- 测试标准计算机视觉超参数的临床可信性.
- 开发临床相关性的局部化标准.
主要成果:
- 通常应用的物体检测指标表现出高的中心间变性.
- 标准的计算机视觉超参数并不总是产生临床上可信的结果.
- 新的局部化标准显示出与临床相关性的良好相关性.
结论:
- 在聚合物检测中的性能结果对设计选择非常敏感.
- 目前的度量配置往往无法满足临床需求,因为超参数低于最佳值.
- 在数据集中比较性能可能会产生误导性,突出显示了基于深度学习的结肠镜检查中需要修改验证策略的需要.
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