Here you can openly access the main publication behind the Rankings Reloaded toolkit.
Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond.
Please cite the following paper if you use our online tool for a publication:
Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Aguilera Saiz, L., Cardoso, M.J., Maier-Hein, L. and Kopp-Schneider, A. Methods and open-source toolkit for analyzing and visualizing challenge results. Sci Rep 11, 2369 (2021). https://doi.org/10.1038/s41598-021-82017-6
Bibtex entry:
@Article{Wiesenfarth2021,
title = {Methods and open-source toolkit for analyzing and visualizing challenge results},
author = {Wiesenfarth, Manuel and Reinke, Annika and Landman, Bennett A and Eisenmann, Matthias and Saiz, Laura Aguilera and Cardoso, M Jorge and Maier-Hein, Lena and Kopp-Schneider, Annette},
journal = {Scientific Reports},
publisher = {Springer Science and Business Media LLC},
year = {2021},
month = {Jan},
day = {27},
volume = {11},
number = {1},
pages = {2369},
issn = {2045-2322},
doi = {10.1038/s41598-021-82017-6},
url = {https://doi.org/10.1038/s41598-021-82017-6}
}