Why is the winner the best?

Conference · 01 January 2023

DOI:https://doi.org/10.1109/CVPR52729.2023.01911· OSTI ID:2005225

Eisenmann, Matthias; Reinke, Annika; Weru, V.; Tizabi, M. D.; Isensee, F.; Adler, T. J.; Ali, S.; Andrearczyk, V.; Aubreville, M.; Baid, U.; Xia, Fangfang

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi- center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), im- age preprocessing (97%), data curation (79%), and post- processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

• Research Organization: Argonne National Laboratory (ANL)
• Sponsoring Organization: Helmholtz Association of German Research Centres; National Institutes of Health (NIH); Netherlands Organisation for Scientific Research (NWO); Engineering and Physical Sciences Research Council (EPSRC); Wellcome Trust; India Science and Engineering Research Board (SERB); European Union - Horizon 2020 Research and Innovation Programme; Deutsche Forschungsgemeinschaft (DFG); Agence Nationale de la recherche (ANR)
• DOE Contract Number: AC02-06CH11357
• OSTI ID: 2005225
• Country of Publication: United States
• Language: English