  Most benchmark datasets for computer vision contain irrelevant images, near
duplicates, and label errors. Consequently, model performance on these
benchmarks may not be an accurate estimate of generalization capabilities. This
is a particularly acute concern in computer vision for medicine where datasets
are typically small, stakes are high, and annotation processes are expensive
and error-prone. In this paper we propose SelfClean, a general procedure to
clean up image datasets exploiting a latent space learned with
self-supervision. By relying on self-supervised learning, our approach focuses
on intrinsic properties of the data and avoids annotation biases. We formulate
dataset cleaning as either a set of ranking problems, which significantly
reduce human annotation effort, or a set of scoring problems, which enable
fully automated decisions based on score distributions. We demonstrate that
SelfClean achieves state-of-the-art performance in detecting irrelevant images,
near duplicates, and label errors within popular computer vision benchmarks,
retrieving both injected synthetic noise and natural contamination. In
addition, we apply our method to multiple image datasets and confirm an
improvement in evaluation reliability.
