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：验证度量是可靠追踪科学进展以及弥合人工智能（AI）研究与其实际应用转化之间鸿沟的关键。然而，越来越多的证据表明，特别是在图像分析领域，度量往往与研究问题本身的选择不匹配。这可能归因于度量相关知识的可获取性不足：虽然考虑验证度量的各自优势、劣势和局限性是做出明智选择的必要前提，但相关知识目前分散且难以被个体研究者获取。基于多学科专家联盟进行的多轮德尔菲法过程以及广泛的社区反馈，本文首次提供了关于图像分析验证中度量相关陷阱的可靠且全面的共通信息入口。本文聚焦于生物医学图像分析，但具备向其他领域推广的潜力，所涉及的陷阱跨应用领域泛化，并依据新创建的领域无关分类法进行归纳。为便于理解，每个陷阱均附有图示和具体案例。作为面向各类专业水平研究人员的结构化知识体系，本文增强了对图像分析验证这一关键主题的全球性理解。