BIAS: Transparent reporting of biomedical image analysis challenges
Open accessAbstract
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 claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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].”
From AI in Medical Imaging Informatics: Current Challenges and Future Directions (2020) · cites this paper as [84]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,361 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
Artificial intelligence bias in medical system designs: a systematic review
Kumar, A., Aelgani, V., Vohra, R., Gupta, S. K., Bhagawati, M., Paul, S., Saba, L., Suri, N., Khanna, N. N., Laird, J. R., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16029-x · Cited by 65 · see citing works
- 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)
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2020-12
- Volume
- 66
- Pages
- 101796
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7441980
- 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
- hybrid
- doi.org
- Licence
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review