Metrics reloaded: recommendations for image analysis validation

Item Type:	Review
Title:	Metrics reloaded: recommendations for image analysis validation
Creators Name:	Maier-Hein, L. and Reinke, A. and Godau, P. and Tizabi, M.D. and Buettner, F. and Christodoulou, E. and Glocker, B. and Isensee, F. and Kleesiek, J. and Kozubek, M. and Reyes, M. and Riegler, M.A. and Wiesenfarth, M. and Kavur, A.E. and Sudre, C.H. and Baumgartner, M. and Eisenmann, M. and Heckmann-Nötzel, D. and Rädsch, T. and Acion, L. and Antonelli, M. and Arbel, T. and Bakas, S. and Benis, A. and Blaschko, M.B. and Cardoso, M.J. and Cheplygina, V. and Cimini, B.A. and Collins, G.S. and Farahani, K. and Ferrer, L. and Galdran, A. and van Ginneken, B. and Haase, R. and Hashimoto, D.A. and Hoffman, M.M. and Huisman, M. and Jannin, P. and Kahn, C.E. and Kainmueller, D. and Kainz, B. and Karargyris, A. and Karthikesalingam, A. and Kofler, F. and Kopp-Schneider, A. and Kreshuk, A. and Kurc, T. and Landman, B.A. and Litjens, G. and Madani, A. and Maier-Hein, K. and Martel, A.L. and Mattson, P. and Meijering, E. and Menze, B. and Moons, K.G.M. and Müller, H. and Nichyporuk, B. and Nickel, F. and Petersen, J. and Rajpoot, N. and Rieke, N. and Saez-Rodriguez, J. and Sánchez, C.I. and Shetty, S. and van Smeden, M. and Summers, R.M. and Taha, A.A. and Tiulpin, A. and Tsaftaris, S.A. and Van Calster, B. and Varoquaux, G. and Jäger, P.F.
Abstract:	Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. In biomedical image analysis, chosen performance metrics often do not reflect the domain interest, and thus fail to adequately measure scientific progress and hinder translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. Developed by a large international consortium in a multistage Delphi process, it 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), dataset and algorithm output. On the basis of 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 classification tasks 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. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases.
Keywords:	Algorithms, Computer-Assisted Image Processing, Machine Learning, Semantics
Source:	Nature Methods
ISSN:	1548-7091
Publisher:	Nature Publishing Group
Volume:	21
Number:	2
Page Range:	195-212
Date:	12 February 2024
Official Publication:	https://doi.org/10.1038/s41592-023-02151-z
PubMed:	View item in PubMed

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