Metrics reloaded: Pitfalls and recommendations for image analysis validation

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, a large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers towards choosing metrics in a problem-aware manner. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. The framework was developed in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint - a structured representation of the given problem that captures all aspects that are relevant for metric selection from the domain interest to the properties of the target structure(s), data set and algorithm output. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. Users are guided through the process of selecting and applying appropriate validation metrics while being made aware of potential pitfalls. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a common point of access to explore weaknesses and strengths of the most common validation metrics. An instantiation of the framework for various biological and medical image analysis use cases demonstrates its broad applicability across domains.