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 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

Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future

How to exploit weaknesses in biomedical challenge design and organization

\u3cp\u3eSince the first MICCAI grand challenge organized in 2007 in Brisbane, challenges have become an integral part of MICCAI conferences. In the meantime, challenge datasets have become widely recognized as international benchmarking datasets and thus have a great influence on the research community and individual careers. In this paper, we show several ways in which weaknesses related to current challenge design and organization can potentially be exploited. Our experimental analysis, based on MICCAI segmentation challenges organized in 2015, demonstrates that both challenge organizers and participants can potentially undertake measures to substantially tune rankings. To overcome these problems we present best practice recommendations for improving challenge design and organization.\u3c/p\u3

How to exploit weaknesses in biomedical challenge design and organization

Since the first MICCAI grand challenge organized in 2007 in Brisbane, challenges have become an integral part of MICCAI conferences. In the meantime, challenge datasets have become widely recognized as international benchmarking datasets and thus have a great influence on the research community and individual careers. In this paper, we show several ways in which weaknesses related to current challenge design and organization can potentially be exploited. Our experimental analysis, based on MICCAI segmentation challenges organized in 2015, demonstrates that both challenge organizers and participants can potentially undertake measures to substantially tune rankings. To overcome these problems we present best practice recommendations for improving challenge design and organization

Author correction:Why rankings of biomedical image analysis competitions should be interpreted with care (Nature Communications, (2018), 9, 1, (5217), 10.1038/s41467-018-07619-7)

\u3cp\u3eIn the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.\u3c/p\u3

Why rankings of biomedical image analysis competitions should be interpreted with care

International challenges have become the standard for validation of biomedical image analysis methods. Given their scientific impact, it is surprising that a critical analysis of common practices related to the organization of challenges has not yet been performed. In this paper, we present a comprehensive analysis of biomedical image analysis challenges conducted up to now. We demonstrate the importance of challenges and show that the lack of quality control has critical consequences. First, reproducibility and interpretation of the results is often hampered as only a fraction of relevant information is typically provided. Second, the rank of an algorithm is generally not robust to a number of variables such as the test data used for validation, the ranking scheme applied and the observers that make the reference annotations. To overcome these problems, we recommend best practice guidelines and define open research questions to be addressed in the future

Surgical data science – from concepts toward clinical translation

International audienceRecent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process