Survey on Biomedical Image Analysis challenges ¶
By: p0089338 on July 28, 2021, 8:04 p.m.
Dear participants,
Together with your submission, we kindly ask you to submit a questionnaire that is prepared by a new initiative with respect to biomedical challenges. This initiative involves many research institutions and is led by the MICCAI special interest group on biomedical image analysis challenges (contact person: Lena Maier-Hein, German Cancer Research Center (DKFZ)). We were asked for our support, and we have decided to provide it.
What is the initiative about? In the past few years, the initiative has been working on bringing biomedical image analysis to the next level of quality [1, 2, 3, 4, 5]. While the focus was on the meta research question “Is the winner really the best?”, the goal now is to go one step further and analyze challenge participation characteristics (e.g. expertise of team, algorithm design, computational infrastructure used). To this end, we are planning to perform a meta-analysis of the challenges conducted in 2021 (ISBI and MICCAI).
As in the previous Nature Communications paper [1], the results will be presented in an anonymized and aggregated fashion, such that findings are generally not linked to specific challenges.
As a further incentive, the initiative is pleased to offer you a co-authorship on the arXiv publication of the statistical analysis. Careful completion of the survey will be a prerequisite for co-authorship. Additionally, you can choose to be considered for prizes that will be raffled among the pool of ISBI and MICCAI 2021 challenge participants that submit the questionnaire.
Please visit the following link to complete the survey: SURVEY
References:
[1] Maier-Hein, L., Eisenmann, M., Reinke, A., Onogur, S., Stankovic, M., Scholz, P., Arbel, T., Bogunovic, H., Bradley, A.P., Carass, A., Feldmann, C., Frangi, A.F., Full, P.M., van Ginneken, B., Hanbury, A., Honauer, K., Kozubek, M., Landman, B.A., März, K., Maier, O., Maier-Hein, K., Menze, B.H., Müller, H., Neher, P.F., Niessen, W., Rajpoot, N., Sharp, G.C., Sirinukunwattana, K., Speidel, S., Stock, C., Stoyanov, D., Taha, A.A., van der Sommen, F., Wang, C.-W., Weber, M.-A., Zheng, G., Jannin, P., Kopp-Schneider, A., 2018. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat. Commun. 9, 5217. https://doi.org/10.1038/s41467-018-07619-7
[2] Reinke, A., Eisenmann, M., Onogur, S., Stankovic, M., Scholz, P., Full, P.M., Bogunovic, H., Landman, B.A., Maier, O., Menze, B., Sharp, G.C., Sirinukunwattana, K., Speidel, S., van der Sommen, F., Zheng, G., Müller, H., Kozubek, M., Arbel, T., Bradley, A.P., Jannin, P., Kopp-Schneider, A., Maier-Hein, L., 2018. How to Exploit Weaknesses in Biomedical Challenge Design and Organization, in: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Springer International Publishing, Cham, pp. 388–395.
[3] Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A.L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S., Saez-Rodriguez, J., van Ginneken, B., Kopp-Schneider, A., Landman, B.A., 2020. BIAS: Transparent reporting of biomedical image analysis challenges. Med. Image Anal. 66, 101796. https://doi.org/10.1016/j.media.2020.101796
[4] Wiesenfarth, M., Reinke, A., Landman, B.A., Eisenmann, M., Aguilera Saiz, L., Cardoso, M.J., Maier-Hein, L., Kopp-Schneider, A., 2021. Methods and open-source toolkit for analyzing and visualizing challenge results. Sci. Rep. 11, 1–15. https://doi.org/10.1038/s41598-021-82017-6
[5] Roß, T., Bruno, P., Reinke, A., Wiesenfarth, M., Koeppel, L., Full, P.M., Pekdemir, B., Godau, P., Trofimova, D., Isensee, F., Moccia, S., Calimeri, F., Müller-Stich, B.P., Kopp-Schneider, A., Maier-Hein, L., 2021. How can we learn (more) from challenges? A statistical approach to driving future algorithm development. ArXiv210609302 Cs.
