Imperfections in data annotation, known as label noise, are detrimental to
the training of machine learning models and have an often-overlooked
confounding effect on the assessment of model performance. Nevertheless,
employing experts to remove label noise by fully re-annotating large datasets
is infeasible in resource-constrained settings, such as healthcare. This work
advocates for a data-driven approach to prioritising samples for re-annotation
- which we term "active label cleaning". We propose to rank instances according
to estimated label correctness and labelling difficulty of each sample, and
introduce a simulation framework to evaluate relabelling efficacy. Our
experiments on natural images and on a new medical imaging benchmark show that
cleaning noisy labels mitigates their negative impact on model training,
evaluation, and selection. Crucially, the proposed active label cleaning
enables correcting labels up to 4 times more effectively than typical random
selection in realistic conditions, making better use of experts' valuable time
for improving dataset quality.

本文提出一种基于数据驱动的主动标签清理方法来解决数据注释中的标签噪音问题，通过对样本进行优先级排序，提高数据集质量，具有较好的可行性和高效性。