  Deep neural networks (DNNs) have achieved great success in a wide variety of
medical image analysis tasks. However, these achievements indispensably rely on
the accurately-annotated datasets. If with the noisy-labeled images, the
training procedure will immediately encounter difficulties, leading to a
suboptimal classifier. This problem is even more crucial in the medical field,
given that the annotation quality requires great expertise. In this paper, we
propose an effective iterative learning framework for noisy-labeled medical
image classification, to combat the lacking of high quality annotated medical
data. Specifically, an online uncertainty sample mining method is proposed to
eliminate the disturbance from noisy-labeled images. Next, we design a sample
re-weighting strategy to preserve the usefulness of correctly-labeled hard
samples. Our proposed method is validated on skin lesion classification task,
and achieved very promising results.
