Why is the Winner the Best?
Eisenmann, M.; Reinke, A.; Weru, V.; Tizabi, M. D.; Isensee, F.; Adler, T. J.; Ali, S.; Andrearczyk, V.; Aubreville, M.; Baid, U.; Bakas, S.; Balu, N.; Bano, S.; Bernal, J.; Bodenstedt, S.; Casella, A.; Cheplygina, V.; Daum, M.; De Bruijne, M.; Depeursinge, A.; Dorent, R.; Egger, J.; Ellis, D. G.; Engelhardt, S.; Ganz, M.; Ghatwary, N.; Girard, G.; Godau, P.; Gupta, A.; Hansen, L.; Harada, K.; Heinrich, M.; Heller, N.; Hering, A.; Huaulmé, A.; Jannin, P.; Kavur, A. E.; Kodym, O.; Kozubek, M.; Li, J.; Li, H.; Ma, J.; Martín-Isla, C.; Menze, B.; Noble, A.; Oreiller, V.; Padoy, N.; Pati, S.; Payette, K.; Rädsch, T.; Rafael-Patiño, J.; Bawa, V. Singh; Speidel, S.; Sudre, C. H.; Van Wijnen, K.; Wagner, M.; Wei, D.; Yamlahi, A.; Yap, M. H.; Yuan, C.; Zenk, M.; Zia, A.; Zimmerer, D.; Aydogan, D.; Bhattarai, B.; Bloch, L.; Brüngel, R.; Cho, J.; Choi, C.; Dou, Q.; Ezhov, I.; Friedrich, C. M.; Fuller, C.; Gaire, R. R.; Galdran, A.; García Faura, A.; Grammatikopoulou, M.; Hong, S.; Jahanifar, M.; Jang, I.; Kadkhodamohammadi, A.; Kang, I.; Kofler, F.; Kondo, S.; Kuijf, H.; Li, M.; Luu, M.; Martinčič, T.; Morais, P.; Naser, M. A.; Oliveira, B.; Owen, D.; Pang, S.; Park, J.; Park, S.; Płotka, S.; Puybareau, E.; Rajpoot, N.; Ryu, K.; Saeed, N.; Shephard, A.; Shi, P.; Štepec, D.; Subedi, R.; Tochon, G.; Torres, H. R.; Urien, H.; Vilaça, J. L.; Wahid, K. A.; Wang, H.; Wang, J.; Wang, L.; Wang, X.; Wiestler, B.; Wodzinski, M.; Xia, F.; Xie, J.; Xiong, Z.; Yang, S.; Yang, Y.; Zhao, Z.; Maier-Hein, K.; Jäger, P. F.; Kopp-Schneider, A.; Maier-Hein, L.
(2023)
Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, volume 2023-June, pp. 19955 - 19966
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2023-June, pp. 19955 - 19966
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, volume 2023-June, pp. 19955 - 19966
(Part of book)
Abstract
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
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competing method? To address this gap in the literature, we performed a multicenter 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%), image 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.
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Keywords: cell microscopy, Medical and biological vision, Software, Computer Vision and Pattern Recognition
ISSN: 1063-6919
ISBN: 9798350301298
Publisher: IEEE Computer Society Press
Note: Publisher Copyright: © 2023 IEEE.
(Peer reviewed)