Preprint

DKFZ-2023-01783

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Understanding metric-related pitfalls in image analysis validation

Reinke, A.DKFZ* ; Tizabi, M. D.DKFZ* ; Baumgartner, M.DKFZ* ; Eisenmann, M.DKFZ* ; Heckmann-Nötzel, D.DKFZ* ; Kavur, A. E.DKFZ* ; Rädsch, T.DKFZ* ; Sudre, C. H. ; Acion, L. ; Antonelli, M. ; Arbel, T. ; Bakas, S. ; Benis, A. ; Blaschko, M. ; Büttner, F.DKFZ* ; Cardoso, M. J. ; Cheplygina, V. ; Chen, J. ; Christodoulou, E.DKFZ* ; Cimini, B. A. ; Collins, G. S. ; Farahani, K. ; Ferrer, L. ; Galdran, A. ; van Ginneken, B. ; Glocker, B. ; Godau, P.DKFZ* ; Haase, R. ; Hashimoto, D. A. ; Hoffman, M. M. ; Huisman, M. ; Isensee, F.DKFZ* ; Jannin, P. ; Kahn, C. E. ; Kainmueller, D. ; Kainz, B. ; Karargyris, A. ; Karthikesalingam, A. ; Kenngott, H. ; Kleesiek, J.DKFZ* ; Kofler, F. ; Kooi, T. ; Kopp-Schneider, A.DKFZ* ; Kozubek, M. ; Kreshuk, A. ; Kurc, T. ; Landman, B. A. ; Litjens, G. ; Madani, A. ; Maier-Hein, K.DKFZ* ; Martel, A. L. ; Mattson, P. ; Meijering, E. ; Menze, B. ; Moons, K. G. M. ; Müller, H. ; Nichyporuk, B. ; Nickel, F. ; Petersen, J. ; Rafelski, S. M. ; Rajpoot, N. ; Reyes, M. ; Riegler, M. A. ; Rieke, N. ; Saez-Rodriguez, J.DKFZ* ; Sánchez, C. I. ; Shetty, S. ; van Smeden, M. ; Summers, R. M. ; Taha, A. A. ; Tiulpin, A. ; Tsaftaris, S. A. ; Van Calster, B. ; Varoquaux, G. ; Wiesenfarth, M.DKFZ* ; Yaniv, Z. R. ; Jäger, P. F.DKFZ* ; Maier-Hein, L.DKFZ*

2023
arXiv

Report No.: arXiv:2302.01790

Abstract: Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately in relation to the underlying research problem. This could be attributed to a lack of accessibility of metric-related knowledge: While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Focusing on biomedical image analysis but with the potential of transfer to other fields, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. To facilitate comprehension, illustrations and specific examples accompany each pitfall. As a structured body of information accessible to researchers of all levels of expertise, this work enhances global comprehension of a key topic in image analysis validation.

Keyword(s): Computer Vision and Pattern Recognition (cs.CV) ; FOS: Computer and information sciences

Note: arXiv:2302.01790 [cs.CV] (or arXiv:2302.01790v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2302.01790

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Contributing Institute(s):

1. E130 Intelligente Medizinische Systeme (E130)
2. C060 Biostatistik (C060)
3. E230 Medizinische Bildverarbeitung (E230)
4. NWG Interaktives maschinelles Lernen (E290)

Research Program(s):

1. 315 - Bildgebung und Radioonkologie (POF4-315) (POF4-315)

Appears in the scientific report 2023

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