Metrics reloaded: Recommendations for image analysis validation (2206.01653v8)

Abstract: Increasing evidence shows that flaws in 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, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. 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. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. 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. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool, which also provides a point of access to explore weaknesses, strengths and specific recommendations for the most common validation metrics. The broad applicability of our framework across domains is demonstrated by an instantiation for various biological and medical image analysis use cases.

• The paper introduces a novel problem fingerprinting approach that tailors metric selection for specific biomedical image challenges.
• It details a decision-tree methodology aligning problem characteristics with optimal validation metrics.
• The framework addresses common pitfalls and bridges the gap between machine learning research and clinical application.

Recommendations for Image Analysis Validation

The paper "Metrics Reloaded: Recommendations for Image Analysis Validation" proposes a comprehensive framework tailored to guide the selection of appropriate validation metrics in the field of automatic biomedical image analysis. The initiative arises from the recognition that existing validation metrics often misalign with the specificities of biomedical problems, thus impeding scientific progress and obstructing the clinical translation of ML advancements.

Problem-Focused Validation Framework

Central to the framework is the innovative "problem fingerprinting" concept, designed to encapsulate all facets pertinent to metric selection, ranging from domain interests to attributes of target structures, data characteristics, and expected algorithm outputs. This structured approach is crucial for accommodating the nuanced requirements inherent in biomedical image analysis tasks.

The process outlined revolves around several key components:

1. Problem Category Identification: Mapping a given biomedical challenge to the appropriate image analysis problem category—image-level classification, object detection, semantic segmentation, or instance segmentation. This step is crucial in avoiding common misalignments where, for instance, object detection tasks are incorrectly framed as segmentation tasks.
2. Fingerprint Generation: Entails capturing domain interest-related considerations (such as boundary importance or size relevance), target structure characteristics (like size variability or shape complexity), dataset traits (such as class imbalance presence), and algorithmic properties (for example, availability of score predictions).
3. Metric Selection: Leverages the problem fingerprint to navigate a decision tree that guides the selection of suitable metrics from a pre-defined pool, ensuring that chosen metrics are aligned with the specific problem characteristics.
4. Application of Metrics: The final step involves the proper application of these metrics to a dataset, with detailed guidance provided to circumnavigate common pitfalls in the implementation, aggregation, and interpretation of results.

Addressing Validation Pitfalls

The "Metrics Reloaded" framework explicitly targets three core categories of common pitfalls in metric selection: inappropriate problem category choice, ill-suited metric selection, and flawed metric application. Notably, the paper sheds light on the often-overlooked implications of metric selection errors, including wasted resources in research directions driven by misleading metrics and the failure to translate ML solutions into practical applications due to validation misalignment.

Future Implications and Utility

This paper underscores the necessity for rigorous, problem-centric validation methodologies, especially as ML methodologies converge across application domains. The formulation of this framework and its implementation as an online tool paves the way for setting a new standard in constructing and validating biomedical image analysis algorithms with precision.

The consortium envisions that the standardization brought about by Metrics Reloaded will catalyze more reliable tracking of scientific advancements and facilitate bridging the gap between ML research innovations and tangible clinical practices. It also opens pathways for cross-domain synergies by anchoring metric selection in a structured, problem-informed manner rather than relying on historically influenced practices.

In conclusion, "Metrics Reloaded" not only lays out a robust and detailed strategy for the validation of biomedical image analysis but also calls for a paradigm shift towards a conscientious selection of metrics that genuinely reflect and serve the scientific and practical needs inherent in the field.