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@ARTICLE{MaierHein:163703,
author = {L. Maier-Hein$^*$ and A. Reinke$^*$ and M. Kozubek and A.
L. Martel and T. Arbel and M. Eisenmann$^*$ and A. Hanbury
and P. Jannin and H. Müller and S. Onogur and J.
Saez-Rodriguez and B. van Ginneken and A. Kopp-Schneider$^*$
and B. A. Landman},
title = {{BIAS}: {T}ransparent reporting of biomedical image
analysis challenges.},
journal = {Medical image analysis},
volume = {66},
issn = {1361-8415},
address = {Amsterdam [u.a.]},
publisher = {Elsevier Science},
reportid = {DKFZ-2020-01979},
pages = {101796},
year = {2020},
note = {#EA:E130#},
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.},
cin = {E130 / C060},
ddc = {610},
cid = {I:(DE-He78)E130-20160331 / I:(DE-He78)C060-20160331},
pnm = {315 - Imaging and radiooncology (POF3-315)},
pid = {G:(DE-HGF)POF3-315},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:32911207},
doi = {10.1016/j.media.2020.101796},
url = {https://inrepo02.dkfz.de/record/163703},
}