了解图像分析验证中的与度量相关的陷
Annika Reinke1,2,3, Minu D Tizabi4,5, Michael Baumgartner6
1German Cancer Research Center (DKFZ) Heidelberg, Division of Intelligent Medical Systems, Heidelberg, Germany. a.reinke@dkfz-heidelberg.de.
Nature methods
|February 12, 2024
概括
选择适当的验证指标对于科学进步至关重要,特别是在AI图像分析中. 本研究确定了衡量标准选择中的常见陷,以提高研究人员信息的可靠性和可访问性.
科学领域:
- 人工智能的人工智能是人工智能.
- 生物医学图像分析
- 科学验证科学验证
背景情况:
- 验证指标对于科学进步和人工智能翻译至关重要.
- 不充分的度量选择,特别是在图像分析中,是一个越来越令人担忧的问题.
- 关于验证指标限制的现有知识是分散的,难以获得的.
研究的目的:
- 为图像分析的验证指标提供一个集中,可靠的资源.
- 加强对科学研究中的验证指标的理解和选择.
- 通过更好的验证,解决人工智能研究与其实际应用之间的差距.
主要方法:
- 一个多阶段的Delphi过程,涉及一个多学科专家联盟.
- 广泛的社区反被纳入,以改进发现.
- 为分类陷开发一个域异性分类学.
主要成果:
- 识别和分类用于图像分析的验证指标中常见的陷.
- 为研究人员创建一个全面的资源.
- 在各种应用领域普遍存在的陷,不仅限于生物医学成像.
结论:
- 对验证指标陷的更好理解提高了科学严谨性.
- 对于人工智能翻译来说,关于度量选择的可访问,可靠的信息至关重要.
- 对验证的标准化理解是推动图像分析研究的关键.
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