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BIAS: Transparent reporting of biomedical image analysis challenges

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 pr...

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Detalles Bibliográficos
Autores principales: Maier-Hein, Lena, Reinke, Annika, Kozubek, Michal, Martel, Anne L., Arbel, Tal, Eisenmann, Matthias, Hanbury, Allan, Jannin, Pierre, Müller, Henning, Onogur, Sinan, Saez-Rodriguez, Julio, van Ginneken, Bram, Kopp-Schneider, Annette, Landman, Bennett A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier B.V. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441980/
https://www.ncbi.nlm.nih.gov/pubmed/32911207
http://dx.doi.org/10.1016/j.media.2020.101796
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author Maier-Hein, Lena
Reinke, Annika
Kozubek, Michal
Martel, Anne L.
Arbel, Tal
Eisenmann, Matthias
Hanbury, Allan
Jannin, Pierre
Müller, Henning
Onogur, Sinan
Saez-Rodriguez, Julio
van Ginneken, Bram
Kopp-Schneider, Annette
Landman, Bennett A.
author_facet Maier-Hein, Lena
Reinke, Annika
Kozubek, Michal
Martel, Anne L.
Arbel, Tal
Eisenmann, Matthias
Hanbury, Allan
Jannin, Pierre
Müller, Henning
Onogur, Sinan
Saez-Rodriguez, Julio
van Ginneken, Bram
Kopp-Schneider, Annette
Landman, Bennett A.
author_sort Maier-Hein, Lena
collection PubMed
description 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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spelling pubmed-74419802020-08-24 BIAS: Transparent reporting of biomedical image analysis challenges Maier-Hein, Lena Reinke, Annika Kozubek, Michal Martel, Anne L. Arbel, Tal Eisenmann, Matthias Hanbury, Allan Jannin, Pierre Müller, Henning Onogur, Sinan Saez-Rodriguez, Julio van Ginneken, Bram Kopp-Schneider, Annette Landman, Bennett A. Med Image Anal Article 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. The Authors. Published by Elsevier B.V. 2020-12 2020-08-21 /pmc/articles/PMC7441980/ /pubmed/32911207 http://dx.doi.org/10.1016/j.media.2020.101796 Text en © 2020 The Authors. Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Maier-Hein, Lena
Reinke, Annika
Kozubek, Michal
Martel, Anne L.
Arbel, Tal
Eisenmann, Matthias
Hanbury, Allan
Jannin, Pierre
Müller, Henning
Onogur, Sinan
Saez-Rodriguez, Julio
van Ginneken, Bram
Kopp-Schneider, Annette
Landman, Bennett A.
BIAS: Transparent reporting of biomedical image analysis challenges
title BIAS: Transparent reporting of biomedical image analysis challenges
title_full BIAS: Transparent reporting of biomedical image analysis challenges
title_fullStr BIAS: Transparent reporting of biomedical image analysis challenges
title_full_unstemmed BIAS: Transparent reporting of biomedical image analysis challenges
title_short BIAS: Transparent reporting of biomedical image analysis challenges
title_sort bias: transparent reporting of biomedical image analysis challenges
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441980/
https://www.ncbi.nlm.nih.gov/pubmed/32911207
http://dx.doi.org/10.1016/j.media.2020.101796
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