Why rankings of biomedical image analysis competitions should be interpreted with care (1806.02051v2)

Abstract: 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.

• The paper presents an empirical analysis of 150 biomedical image challenges, highlighting inadequate reporting and diverse evaluation metrics.
• It reveals that minor adjustments in ranking methods can lead to major outcome variations, questioning the robustness of competition rankings.
• The authors advocate for a structured reporting framework with 53 parameters to enhance transparency and reproducibility in challenge designs.

A Critical Analysis of Common Research Practices in Biomedical Image Analysis Competitions

The paper examines the practices of biomedical image analysis challenges, an essential part of method validation in the field. An empirical evaluation was conducted on 150 such challenges organized until 2016, shedding light on significant shortcomings and variability in their design and execution.

Key Findings

1. Prevalence and Diversity: These challenges have become increasingly integral to the field of biomedical image analysis. They cover various problems, such as segmentation and classification, predominantly using imaging modalities like MRI and CT. However, the reporting practices remain inadequate, often failing to ensure reproducibility and meaningful cross-comparison of results. Notably, only a small fraction of the necessary information for interpreting results is consistently reported.
2. Challenges in Challenge Design: The design of these challenges is marked by a lack of standardization, particularly in the reporting and evaluation metrics. With 97 different metrics recorded, many challenges rely on unique or unrepeated metrics, leading to inconsistencies. Such variabilities render rankings sensitive to design parameters, including the choice of metric and aggregation method.
3. Sensitivity and Robustness: The sensitivity analysis of 2015 segmentation challenges illustrated that minor modifications in ranking methods or variances in annotators can lead to significantly different outcomes. In some observed instances, algorithm rankings varied dramatically with different metrics or test cases, undermining the reliability of final ranks.
4. Handling of Missing Data and Rank Manipulation: Alarmingly, 82% of the tasks did not specify how missing data is handled, which could potentially allow rank manipulations. Systems neglectful of this aspect leave room for strategic omissions to falsely elevate rankings.
5. Community and Organizational Feedback: Feedback from a comprehensive international survey underscored the demand for more robust design guidelines and quality control processes in biomedical challenges. Researchers highlighted significant issues such as data representativeness, quality of reference data, and lack of evaluation transparency.

Implications and Recommendations

An overarching recommendation is the comprehensive reporting of challenge designs and results to enhance transparency and reproducibility. The authors advocate for a structured reporting framework entailing 53 essential parameters designed to mitigate prevailing challenges. This paper calls for community engagement to establish internationally recognized standards and feedback mechanisms to ensure high-quality challenge designs. Fundamental to this is the creation of incentives for robust challenge planning and execution.

The insights presented have overarching implications beyond biomedical image analysis, relevant to other validation-oriented fields that rely on well-conducted and reproducible comparisons. This critical analysis provides a blueprint for organizing and reporting challenges, aiming to maximize their validity as benchmarking tools.

Future Directions

Future research should address open questions related to data representativeness, optimal metric selection, and the challenges of ranking scheme design. As discussions around standardization and best practices continue, the field is poised for developments that might redefine competitive validations and their contribution to scientific knowledge and practical applications. The integration of community-driven solutions and advancements in AI may propel biomedical image analysis challenges towards enhanced scientific contributions and applications in clinical practices.