Metrics reloaded: recommendations for image analysis validation

Area of Science:

• Biomedical Image Analysis
• Machine Learning Validation

Background:

• Inadequate performance metrics in ML algorithm validation impede scientific progress and clinical translation in biomedical image analysis.
• Current validation practices often fail to align with specific domain interests, leading to unreliable assessments of ML model performance.

Purpose of the Study:

• To introduce Metrics Reloaded, a comprehensive framework for guiding researchers in selecting appropriate validation metrics for ML algorithms.
• To address the critical issue of flawed ML algorithm validation in biomedical image analysis.

Main Methods:

• Developed through a multistage Delphi process by an international consortium.
• Introduced the 'problem fingerprint' concept for structured problem representation relevant to metric selection.
• Implemented the framework as an accessible online tool, Metrics Reloaded.

Main Results:

• The Metrics Reloaded framework guides users in selecting and applying suitable validation metrics, highlighting potential pitfalls.
• The framework is applicable to various image analysis tasks, including image-level classification, object detection, semantic segmentation, and instance segmentation.
• Demonstrated applicability across diverse biomedical use cases.

Conclusions:

• Metrics Reloaded promotes standardized and problem-aware validation methodologies in machine learning.
• The framework and associated tool enhance the reliability and translation of ML techniques in biomedical image analysis.