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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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Authors. Published by Elsevier B.V.
2020
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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. |
format | Online Article Text |
id | pubmed-7441980 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | The Authors. Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
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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