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）研究与其实际应用之间鸿沟的关键。然而，越来越多的证据表明，尤其在图像分析领域，度量往往与底层研究问题选择不当。这可能归因于度量相关知识获取不足：尽管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关键前提，但相关知识目前分散且难以被个体研究者便捷获取。基于多学科专家联盟通过多轮德尔菲法过程及广泛社区反馈，本工作首次提供了关于图像分析验证度量相关陷阱的可靠且全面的通用信息入口。本工作聚焦于生物医学图像分析，但具有向其他领域迁移的潜力，所讨论的陷阱跨应用领域泛化，并根据新创建的、领域无关的分类体系进行归类。为便于理解，每个陷阱均附有图示和具体示例。作为可供各级别研究者获取的结构化知识体系，本工作提升了对图像分析验证这一关键主题的全球理解。