BIAS : transparent reporting of biomedical image analysis challenges

Maier-Hein, Lena (German Cancer Research Center, Heidelberg, Germany) ; Reinke, Annika (German Cancer Research Center, Heidelberg, Germany) ; Kozubek, Michal (Masaryk University, Brno, Czech Republic) ; Martel, Anne L. (Sunnybrook Research Institute, Toronto, Canada ; University of Toronto, Canada) ; Arbel, Tal (McGill University, Montréal, Canada) ; Eisenmann, Matthias (German Cancer Research Center, Heidelberg, Germany) ; Hanbury, Allan (Technische Universität (TU) Wien, Vienna, Austria ; Complexity Science Hub Vienna, Austria) ; Jannin, Pierre (Université de Rennes, France) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland) ; Onogur, Sinan (German Cancer Research Center, Heidelberg, Germany) ; Saez-Rodriguez, Julio (Heidelberg University, Germany ; Heidelberg University Hospital, Germany ; Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Germany) ; Van Ginneken, Bram (Radboud University Center, Nijmegen, The Netherlands) ; Kopp-Schneider, Annette (German Cancer Research Center, Heidelberg, Germany) ; Landman, Bennett A. (Vanderbilt University, Nashville, USA)

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. This article describes how the BIAS statement was developed and presents 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.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Informatique
Date:
2020-12
Pagination:
7 p.
Published in:
Medical image analysis
Numeration (vol. no.):
2020, vol. 66, article 101796, pp. 1-7
DOI:
ISSN:
1361-8415
Appears in Collection:



 Record created 2021-01-26, last modified 2021-01-29

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