EISENMANN, M., A. REINKE, V. WERU, M. D. TIZABI, F. ISENSEE, T. J. ADLER, S. ALI, V. ANDREARCZYK, M. AUBREVILLE, U. BAID, S. BAKAS, N. BALU, S. BANO, J. BERNAL, S. BODENSTEDT, A. CASELLA, V. CHEPLYGINA, M. DAUM, M. DE BRUIJNE, A. DEPEURSINGE, R. DORENT, J. EGGER, D. G. ELLIS, S. ENGELHARDT, M. GANZ, N. GHATWARY, G. GIRARD, P. GODAU, A. GUPTA, L. HANSEN, K. HARADA, M. HEINRICH, N. HELLER, A. HERING, A. HUAULME, P. JANNIN, A. E. KAVUR, O. KODYM, Michal KOZUBEK, J. LI, H. LI, J. MA, C. MARTIN-ISLA, B. MENZE, A. NOBLE, V. OREILLER, N. PADOY, S. PATI, K. PAYETTE, T. RAEDSCH, J. RAFAEL-PATINO, V. Singh BAWA, S. SPEIDEL, C. H. SUDRE, K. VAN WIJNEN, M. WAGNER, D. WEI, A. YAMLAHI, M. H. YAP, C. YUAN, M. ZENK, A. ZIA, D. ZIMMERER, D. AYDOGAN, B. BHATTARAI, L. BLOCH, R. BRUENGEL, J. CHO, C. CHOI, Q. DOU, I. EZHOV, C. M. FRIEDRICH, C. FULLER, R. R. GAIRE, A. GALDRAN, A. Garcia FAURA, M. GRAMMATIKOPOULOU, S. HONG, M. JAHANIFAR, I. JANG, A. KADKHODAMOHAMMADI, I. KANG, F. KOFLER, S. KONDO, H. KUIJF, M. LI, M. LUU, T. MARTINCIC, P. MORAIS, M. A. NASER, B. OLIVEIRA, D. OWEN, S. PANG, J. PARK, S. PARK, S. PLOTKA, E. PUYBAREAU, N. RAJPOOT, K. RYU, N. SAEED, A. SHEPHARD, P. SHI, D. STEPEC, R. SUBEDI, G. TOCHON, H. R. TORRES, H. URIEN, J. L. VILACA, K. A. WAHID, H. WANG, J. WANG, L. WANG, X. WANG, B. WIESTLER, M. WODZINSKI, F. XIA, J. XIE, Z. XIONG, S. YANG, Y. YANG, Z. ZHAO, K. MAIER-HEIN, P. F. JAEGER, A. KOPP-SCHNEIDER and L. MAIER-HEIN. Why is the winner the best?. Online. In 979-8-3503-0129-8. 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR). Vancouver: IEEE COMPUTER SOC, 2023, p. 19955-19966. ISBN 979-8-3503-0129-8. Available from: https://dx.doi.org/10.1109/CVPR52729.2023.01911.
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Basic information
Original name Why is the winner the best?
Authors EISENMANN, M., A. REINKE, V. WERU, M. D. TIZABI, F. ISENSEE, T. J. ADLER, S. ALI, V. ANDREARCZYK, M. AUBREVILLE, U. BAID, S. BAKAS, N. BALU, S. BANO, J. BERNAL, S. BODENSTEDT, A. CASELLA, V. CHEPLYGINA, M. DAUM, M. DE BRUIJNE, A. DEPEURSINGE, R. DORENT, J. EGGER, D. G. ELLIS, S. ENGELHARDT, M. GANZ, N. GHATWARY, G. GIRARD, P. GODAU, A. GUPTA, L. HANSEN, K. HARADA, M. HEINRICH, N. HELLER, A. HERING, A. HUAULME, P. JANNIN, A. E. KAVUR, O. KODYM, Michal KOZUBEK (203 Czech Republic, belonging to the institution), J. LI, H. LI, J. MA, C. MARTIN-ISLA, B. MENZE, A. NOBLE, V. OREILLER, N. PADOY, S. PATI, K. PAYETTE, T. RAEDSCH, J. RAFAEL-PATINO, V. Singh BAWA, S. SPEIDEL, C. H. SUDRE, K. VAN WIJNEN, M. WAGNER, D. WEI, A. YAMLAHI, M. H. YAP, C. YUAN, M. ZENK, A. ZIA, D. ZIMMERER, D. AYDOGAN, B. BHATTARAI, L. BLOCH, R. BRUENGEL, J. CHO, C. CHOI, Q. DOU, I. EZHOV, C. M. FRIEDRICH, C. FULLER, R. R. GAIRE, A. GALDRAN, A. Garcia FAURA, M. GRAMMATIKOPOULOU, S. HONG, M. JAHANIFAR, I. JANG, A. KADKHODAMOHAMMADI, I. KANG, F. KOFLER, S. KONDO, H. KUIJF, M. LI, M. LUU, T. MARTINCIC, P. MORAIS, M. A. NASER, B. OLIVEIRA, D. OWEN, S. PANG, J. PARK, S. PARK, S. PLOTKA, E. PUYBAREAU, N. RAJPOOT, K. RYU, N. SAEED, A. SHEPHARD, P. SHI, D. STEPEC, R. SUBEDI, G. TOCHON, H. R. TORRES, H. URIEN, J. L. VILACA, K. A. WAHID, H. WANG, J. WANG, L. WANG, X. WANG, B. WIESTLER, M. WODZINSKI, F. XIA, J. XIE, Z. XIONG, S. YANG, Y. YANG, Z. ZHAO, K. MAIER-HEIN, P. F. JAEGER, A. KOPP-SCHNEIDER and L. MAIER-HEIN.
Edition Vancouver, 2023 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), p. 19955-19966, 12 pp. 2023.
Publisher IEEE COMPUTER SOC
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher United States of America
Confidentiality degree is not subject to a state or trade secret
Publication form electronic version available online
RIV identification code RIV/00216224:14310/23:00133939
Organization unit Faculty of Science
ISBN 979-8-3503-0129-8
ISSN 1063-6919
Doi http://dx.doi.org/10.1109/CVPR52729.2023.01911
UT WoS 001062531304027
Keywords in English cell microscopy; Medical and biological vision
Tags International impact, Reviewed
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 24/4/2024 03:12.
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 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%), 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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