  Supervised training of deep learning models requires large labeled datasets.
There is a growing interest in obtaining such datasets for medical image
analysis applications. However, the impact of label noise has not received
sufficient attention. Recent studies have shown that label noise can
significantly impact the performance of deep learning models in many machine
learning and computer vision applications. This is especially concerning for
medical applications, where datasets are typically small, labeling requires
domain expertise and suffers from high inter- and intra-observer variability,
and erroneous predictions may influence decisions that directly impact human
health. In this paper, we first review the state-of-the-art in handling label
noise in deep learning. Then, we review studies that have dealt with label
noise in deep learning for medical image analysis. Our review shows that recent
progress on handling label noise in deep learning has gone largely unnoticed by
the medical image analysis community. To help achieve a better understanding of
the extent of the problem and its potential remedies, we conducted experiments
with three medical imaging datasets with different types of label noise, where
we investigated several existing strategies and developed new methods to combat
the negative effect of label noise. Based on the results of these experiments
and our review of the literature, we have made recommendations on methods that
can be used to alleviate the effects of different types of label noise on deep
models trained for medical image analysis. We hope that this article helps the
medical image analysis researchers and developers in choosing and devising new
techniques that effectively handle label noise in deep learning.
