  Widely-used public benchmarks are of huge importance to computer vision and
machine learning research, especially with the computational resources required
to reproduce state of the art results quickly becoming untenable. In medical
image computing, the wide variety of image modalities and problem formulations
yields a huge task-space for benchmarks to cover, and thus the widespread
adoption of standard benchmarks has been slow, and barriers to releasing
medical data exacerbate this issue. In this paper, we examine the role that
publicly available data has played in MICCAI papers from the past five years.
We find that more than half of these papers are based on private data alone,
although this proportion seems to be decreasing over time. Additionally, we
observed that after controlling for open access publication and the release of
code, papers based on public data were cited over 60% more per year than their
private-data counterparts. Further, we found that more than 20% of papers using
public data did not provide a citation to the dataset or associated manuscript,
highlighting the "second-rate" status that data contributions often take
compared to theoretical ones. We conclude by making recommendations for MICCAI
policies which could help to better incentivise data sharing and move the field
toward more efficient and reproducible science.
