Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

翻译：验证度量是可靠跟踪科学进展、弥合人工智能（AI）研究与其实际应用转化之间现有鸿沟的关键。然而，越来越多的证据表明（尤其是在图像分析中），度量往往与底层研究问题不相适配。这可能归因于度量相关知识的可获取性不足：尽管考虑验证度量的个体优势、劣势和局限性是做出明智选择的关键前提，但相关知识目前分散分布且个体研究人员难以获取。基于多阶段德尔菲法（由多学科专家联盟实施）及广泛社区反馈，本工作首次提供了关于图像分析验证中与度量相关陷阱的可靠且全面的常识性信息入口。本研究聚焦生物医学图像分析但具有向其他领域迁移的潜力，所论述的陷阱可泛化至各类应用场景，并根据新创建的领域无关分类体系进行归类。为便于理解，每个陷阱均附有示意图和具体示例。作为面向各级研究人员结构化知识体系，本工作强化了全球对图像分析验证中这一关键主题的认知。