Published on 01.03.2024
Advancing Image Analysis Validation: A Comprehensive Approach through Two Pioneering Sister Publications
In the ever-evolving landscape of artificial intelligence and machine learning, the validation of metrics plays a crucial role in tracking scientific progress and bridging the current gap between artificial intelligence research and its translation into real-world applications. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately.
Helmholtz Imaging team members from the Research Unit as well as Engineering and Support Unit at DKFZ together with an international, multidisciplinary expert consortium published two pioneering articles, “Understanding metric-related pitfalls in image analysis validation“ by Annika Reinke, Minu D. Tizabi et al. and “Metrics reloaded: recommendations for image analysis validation” by Lena-Maier-Hein, Annika Reinke et al. These two articles provide a profound insight into the challenges and solutions surrounding validation metrics in image analysis, particularly in the biomedical domain.
With this work, we are making reliable and comprehensive information on problems and pitfalls related to validation metrics in image analysis available to the scientific community for the first time.
Annika Reinke
Although focused on biomedical image analysis, the addressed pitfalls and metrics recommendations generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation.
Bridging Gaps: Understanding Metric-Related Pitfalls in Image Analysis Validation
The first article delves into the crucial role of validation metrics in tracking scientific progress and facilitating the transition of AI research into practical applications. The authors identify a pressing issue – the inadequate selection of metrics in image analysis. Recognizing the scattered and inaccessible nature of relevant knowledge for individual researchers, the article employs a multistage Delphi process conducted by a diverse expert consortium, incorporating extensive community feedback. The result is a comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focused on biomedical image analysis, the article introduces a domain-agnostic taxonomy categorizing pitfalls. By addressing individual strengths, weaknesses, and limitations of validation metrics, the work enhances the global comprehension of this critical topic, providing a solid foundation for researchers in the field.
Access to article in Nature Methods
A Framework for Precision: Metrics Reloaded: Recommendations for Image Analysis Validation
In the second article, the authors present a solution to the global problem of flaws in ML algorithm validation. The article highlights that chosen performance metrics in biomedical image analysis often fail to align with domain interests, impeding scientific progress and hindering the practical application of ML techniques. To overcome this, the authors introduce “Metrics Reloaded,” a comprehensive framework developed through a multistage Delphi process by a large international consortium. This innovative framework is based on the concept of a “problem fingerprint,” providing a structured representation of the problem at hand. It captures all relevant aspects, from domain interests to the characteristics of the target structure(s), dataset, and algorithm output.
Users are guided through a comprehensive set of questions to create a precise fingerprint of their image analysis problem.
Paul Jäger
Metrics Reloaded guides researchers through the process of metric selection, emphasizing problem-aware choices and raising awareness of potential pitfalls. This framework targets image analysis problems at various levels, including image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To enhance user experience, Metrics Reloaded has been implemented in an online tool, demonstrating its applicability across diverse biomedical use cases.
Access to article in Nature Methods
By addressing pitfalls and providing a problem-aware framework, these articles pave the way for researchers to make informed choices, ultimately fostering the convergence of validation methodologies and advancing the field of image analysis.
Rita Strack, scientific editor at Nature Methods, on X about these two papers (screenshot):
