mlsanityData to 14 Oct 2026

Challenge Results Are Not Reproducible

arXiv 2307.07226 ↗First posted 14 Jul 2023cs.CVcited by 2

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Abstract (arXiv)

While clinical trials are the state-of-the-art methods to assess the effect of new medication in a comparative manner, benchmarking in the field of medical image analysis is performed by so-called challenges. Recently, comprehensive analysis of multiple biomedical image analysis challenges revealed large discrepancies between the impact of challenges and quality control of the design and reporting standard. This work aims to follow up on these results and attempts to address the specific question of the reproducibility of the participants methods. In an effort to determine whether alternative interpretations of the method description may change the challenge ranking, we reproduced the algorithms submitted to the 2019 Robust Medical Image Segmentation Challenge (ROBUST-MIS). The leaderboard differed substantially between the original challenge and reimplementation, indicating that challenge rankings may not be sufficiently reproducible.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 7 references that are in the map. First posted on arXiv 14 Jul 2023.

Builds on

3 of 7 references.

References · 3

  1. 2020U-Net and its variants for medical image segmentation: theory and applicationscited by 169
  2. 2018Why rankings of biomedical image analysis competitions should be interpreted with carecited by 91
  3. 2019Methods and open-source toolkit for analyzing and visualizing challenge resultscited by 60

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Citing papers per year: 2025: 2.

2 papers.