Biomedical Image Analysis Challenges (BIAS) Reporting Guideline
Zenodo (CERN European Organization for Nuclear Research)
Abstract
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.
Authors 14
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Affiliation as printed
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
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Affiliation as printed
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
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Affiliation as printed
Centre for Biomedical Image Analysis, Masaryk University
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University of Toronto · Sunnybrook Research Institute
Affiliation as printed
Physical Sciences, Sunnybrook Research Institute; Department Medical Biophysics, University of Toronto
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Affiliation as printed
Centre for Intelligent Machines, McGill University
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Affiliation as printed
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
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TU Wien · Complexity Science Hub
Affiliation as printed
Institute of Information Systems Engineering, Technische Universität (TU) Wien; Complexity Science Hub Vienna, Vienna
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Inserm · Laboratoire Traitement du Signal et de l'Image · Université Rennes 1 · Université de Rennes
Affiliation as printed
Laboratoire Traitement du Signal et de l'Image (LTSI) -UMR_S 1099, Université de Rennes 1, Inserm
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University of Geneva · HES-SO University of Applied Sciences and Arts Western Switzerland · HES-SO Genève
Affiliation as printed
University of Applied Sciences Western Switzerland (HES-SO); Medical Faculty, University of Geneva
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Affiliation as printed
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ)
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Heidelberg University · University Hospital Heidelberg · RWTH Aachen University
Affiliation as printed
Institute of Computational Biomedicine, Heidelberg University; Faculty of Medicine, Heidelberg University Hospital; Joint Research Centre for Computational Biomedicine, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen
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Radboud University Medical Center
Affiliation as printed
Department of Radiology and Nuclear Medicine, Medical Image Analysis, Radboud University Centre
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Affiliation as printed
Division of Biostatistics, German Cancer Research Center (DKFZ)
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Affiliation as printed
Electrical Engineering, Vanderbilt University, Nashville, Tennessee
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