  With the development of deep learning, medical image classification has been
significantly improved. However, deep learning requires massive data with
labels. While labeling the samples by human experts is expensive and
time-consuming, collecting labels from crowd-sourcing suffers from the noises
which may degenerate the accuracy of classifiers. Therefore, approaches that
can effectively handle label noises are highly desired. Unfortunately, recent
progress on handling label noise in deep learning has gone largely unnoticed by
the medical image. To fill the gap, this paper proposes a noise-tolerant
medical image classification framework named Co-Correcting, which significantly
improves classification accuracy and obtains more accurate labels through
dual-network mutual learning, label probability estimation, and curriculum
label correcting. On two representative medical image datasets and the MNIST
dataset, we test six latest Learning-with-Noisy-Labels methods and conduct
comparative studies. The experiments show that Co-Correcting achieves the best
accuracy and generalization under different noise ratios in various tasks. Our
project can be found at: https://github.com/JiarunLiu/Co-Correcting.
