Why rankings of biomedical image analysis competitions should be interpreted with care
This addresses reproducibility issues for researchers and practitioners in biomedical imaging, but it is incremental as it builds on existing critique of challenge practices.
The paper tackles the problem of unreliable rankings in biomedical image analysis competitions, showing that algorithm ranks are not robust to variables like test data and annotation methods.
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.