Cited by 9Medical Image Segmentation TechniquesCell Image Analysis TechniquesArtificial Intelligence in Healthcare and Education

BIAS: Transparent reporting of biomedical image analysis challenges

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Maier-Hein, Lena, Reinke, Annika, Kozubek, Michal, Martel, Anne L., Arbel, Tal, Eisenmann, Matthias, Hanbury, Allan, Jannin, Pierre +6 more·2020·Medical Image Analysis

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. 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.

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  • A recent analysis of challenge design showed that biases exist that question how easy it would be to translate methods to clinical practice.

    “however, a recent analysis of challenge design has showed that biases exist that questions how easy would be to translate methods to clinical practice [84].”

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Authors
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.
Affiliations
  • German Cancer Research Center (DE)
  • Masaryk University (CZ)
  • University of Toronto (CA)
  • McGill University (CA)
  • TU Wien (AT)
  • Inserm (FR)
  • University of Geneva (CH)
  • Vanderbilt University (US)
Venue
Medical Image Analysis
Publisher
Elsevier BV
Published
2020-12
Volume
66
Pages
101796
ISSN
1361-8415 (ISSN-L)
OpenAlex
W2979992932
Cited by
9
References
1
Citations by year
2023: 4 · 2021: 3 · 2020: 2
FWCI
0.87
Type
journal article
Language
en
Open access
Locations
  • Medical Image Analysis (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • HAL (submittedVersion, cc-by-nc-nd)
  • ArODES (HES-SO (https://www.hes-so.ch/)) (submittedVersion)
  • Radboud Repository (Radboud University) (submittedVersion)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • European Research Council (ERC-2015-StG-37960)
  • Natural Science and Engineering Research Council (RGPIN-2016-06283)
  • Canadian Cancer Society (#705772)
  • Ministry of Education, Youth and Sports of the Czech Republic (CZ.02.1.01/0.0/0.0/16_013/0001775)
  • National Science Foundation (1452485)
  • National Institutes of Health (R01-EB017230-01A1)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
reporting guidelines, reproducibility, transparency, peer review, benchmarking, standardization
MeSH terms
Humans, Prognosis, Reproducibility of Results, Biomedical Research, Checklist

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