TY  - JOUR
AU  - Reinke, Annika
AU  - Tizabi, Minu Dietlinde
AU  - Baumgartner, Michael
AU  - Eisenmann, Matthias
AU  - Heckmann-Nötzel, Doreen
AU  - Kavur, Ali Emre
AU  - Rädsch, Tim
AU  - Sudre, Carole H
AU  - Acion, Laura
AU  - Antonelli, Michela
AU  - Arbel, Tal
AU  - Bakas, Spyridon
AU  - Benis, Arriel
AU  - Büttner, Florian
AU  - Cardoso, M Jorge
AU  - Cheplygina, Veronika
AU  - Chen, Jianxu
AU  - Christodoulou, Evangelia
AU  - Cimini, Beth A
AU  - Farahani, Keyvan
AU  - Ferrer, Luciana
AU  - Galdran, Adrian
AU  - van Ginneken, Bram
AU  - Glocker, Ben
AU  - Godau, Patrick
AU  - Hashimoto, Daniel A
AU  - Hoffman, Michael M
AU  - Huisman, Merel
AU  - Isensee, Fabian
AU  - Jannin, Pierre
AU  - Kahn, Charles E
AU  - Kainmueller, Dagmar
AU  - Kainz, Bernhard
AU  - Karargyris, Alexandros
AU  - Kleesiek, Jens
AU  - Kofler, Florian
AU  - Kooi, Thijs
AU  - Kopp-Schneider, Annette
AU  - Kozubek, Michal
AU  - Kreshuk, Anna
AU  - Kurc, Tahsin
AU  - Landman, Bennett A
AU  - Litjens, Geert
AU  - Madani, Amin
AU  - Maier-Hein, Klaus
AU  - Martel, Anne L
AU  - Meijering, Erik
AU  - Menze, Bjoern
AU  - Moons, Karel G M
AU  - Müller, Henning
AU  - Nichyporuk, Brennan
AU  - Nickel, Felix
AU  - Petersen, Jens
AU  - Rafelski, Susanne M
AU  - Rajpoot, Nasir
AU  - Reyes, Mauricio
AU  - Riegler, Michael A
AU  - Rieke, Nicola
AU  - Saez-Rodriguez, Julio
AU  - Sánchez, Clara I
AU  - Shetty, Shravya
AU  - Summers, Ronald M
AU  - Taha, Abdel A
AU  - Tiulpin, Aleksei
AU  - Tsaftaris, Sotirios A
AU  - Van Calster, Ben
AU  - Varoquaux, Gaël
AU  - Yaniv, Ziv R
AU  - Jäger, Paul
AU  - Maier-Hein, Lena
TI  - Understanding metric-related pitfalls in image analysis validation.
JO  - Nature methods
VL  - 21
IS  - 2
SN  - 1548-7091
CY  - London [u.a.]
PB  - Nature Publishing Group
M1  - DKFZ-2024-00338
SP  - 182 - 194
PY  - 2024
N1  - #EA:E130#LA:E130#LA:E290#
AB  - Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence research and its translation into practice. However, increasing evidence shows that, particularly in image analysis, metrics are often chosen inadequately. Although 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 multistage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides a reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. Although focused on biomedical image analysis, the addressed pitfalls 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:38347140
DO  - DOI:10.1038/s41592-023-02150-0
UR  - https://inrepo02.dkfz.de/record/288084
ER  -