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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.
Afiliação
  • Maier-Hein L; Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, Heidelberg 69120, Germany. Electronic address: l.maier-hein@dkfz-heidelberg.de.
  • Reinke A; Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, Heidelberg 69120, Germany.
  • Kozubek M; Centre for Biomedical Image Analysis, Masaryk University, Botanická 68a, Brno 60200, Czech Republic.
  • Martel AL; Physical Sciences, Sunnybrook Research Institute, 2075 Bayview Avenue, Rm M6-609, Toronto ON M4N 3M5, Canada; Department Medical Biophysics, University of Toronto, 101 College St Suite 15-701, Toronto, ON M5G 1L7, Canada.
  • Arbel T; Centre for Intelligent Machines, McGill University, 3480 University Street, McConnell Engineering Building, Room 425, Montreal QC H3A 0E9, Canada.
  • Eisenmann M; Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, Heidelberg 69120, Germany.
  • Hanbury A; Institute of Information Systems Engineering, Technische Universität (TU) Wien, Favoritenstraße 9-11/194-04, Vienna 1040, Austria; Complexity Science Hub Vienna, Josefstädter Straße 39, Vienna 1080, Austria.
  • Jannin P; Laboratoire Traitement du Signal et de l'Image (LTSI) - UMR_S 1099, Université de Rennes 1, Inserm, Rennes, Cedex 35043, France.
  • Müller H; University of Applied Sciences Western Switzerland (HES-SO), Rue du Technopole 3, Sierre 3960, Switzerland; Medical Faculty, University of Geneva, Rue Gabrielle-Perret-Gentil 4, Geneva 1211, Switzerland.
  • Onogur S; Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 223, Heidelberg 69120, Germany.
  • Saez-Rodriguez J; Institute of Computational Biomedicine, Heidelberg University, Faculty of Medicine, Im Neuenheimer Feld 267, Heidelberg 69120, Germany; Heidelberg University Hospital, Im Neuenheimer Feld 267, Heidelberg 69120, Germany; Joint Research Centre for Computational Biomedicine, Rheinisch-Westfälische Tech
  • van Ginneken B; Department of Radiology and Nuclear Medicine, Medical Image Analysis, Radboud University Center, Nijmegen 6525 GA, The Netherlands.
  • Kopp-Schneider A; Division of Biostatistics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 581, Heidelberg, 69120, Germany.
  • Landman BA; Electrical Engineering, Vanderbilt University, Nashville, Tennessee TN 37235-1679, USA.
Med Image Anal ; 66: 101796, 2020 12.
Article em En | MEDLINE | ID: mdl-32911207
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pesquisa Biomédica / Lista de Checagem Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Pesquisa Biomédica / Lista de Checagem Tipo de estudo: Guideline / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article