10.5281/zenodo.4008954
https://zenodo.org/records/4008954
oai:zenodo.org:4008954
Maier-Hein, Lena
Lena
Maier-Hein
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
Reinke, Annika
Annika
Reinke
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
Kozubek, Michal
Michal
Kozubek
Centre for Biomedical Image Analysis, Masaryk University
Martel, Anne L.
Anne L.
Martel
Physical Sciences, Sunnybrook Research Institute; Department Medical Biophysics, University of Toronto
Arbel, Tal
Tal
Arbel
Centre for Intelligent Machines, McGill University
Eisenmann, Matthias
Matthias
Eisenmann
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
Hanbury, Allan
Allan
Hanbury
Institute of Information Systems Engineering, Technische Universität (TU) Wien; Complexity Science Hub Vienna, Vienna
Jannin, Pierre
Pierre
Jannin
Laboratoire Traitement du Signal et de l'Image (LTSI) -UMR_S 1099, Université de Rennes 1, Inserm
Müller, Henning
Henning
Müller
University of Applied Sciences Western Switzerland (HES-SO); Medical Faculty, University of Geneva
Onogur, Sinan
Sinan
Onogur
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
Saez-Rodriguez, Julio
Julio
Saez-Rodriguez
Institute of Computational Biomedicine, Heidelberg University; Faculty of Medicine, Heidelberg University Hospital; Joint Research Centre for Computational Biomedicine, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
van Ginneken, Bram
Bram
van Ginneken
Department of Radiology and Nuclear Medicine, Medical Image Analysis, Radboud University Centre
Kopp-Schneider, Annette
Annette
Kopp-Schneider
Division of Biostatistics, German Cancer Research Center (DKFZ)
Landman, Bennett A.
Bennett A.
Landman
Electrical Engineering, Vanderbilt University, Nashville, Tennessee
Biomedical Image Analysis Challenges (BIAS) Reporting Guideline
Zenodo
2020
Biomedical challenges
Good scientific practice
Biomedical image analysis
Guideline
2020-08-31
10.5281/zenodo.4008953
Creative Commons Attribution No Derivatives 4.0 International
The number of biomedical image analysis challenges organized per year is steadily increasing. These international competitions have the purpose of benchmarking algorithms on common data sets, typically to identify the best method for a given problem. Recent research, however, revealed that common practice related to challenge reporting does not allow for adequate interpretation and reproducibility of results. To address the discrepancy between the impact of challenges and the quality (control), the Biomedical Image Analysis ChallengeS (BIAS) initiative developed a set of recommendations for the reporting of challenges. The BIAS statement aims to improve the transparency of the reporting of a biomedical image analysis challenge regardless of field of application, image modality or task category assessed. We present a checklist which authors of biomedical image analysis challenges are encouraged to include in their submission when giving a paper on a challenge into review. The purpose of the checklist is to standardize and facilitate the review process and raise interpretability and reproducibility of challenge results by making relevant information explicit.