  Deep neural networks have achieved remarkable success in a wide variety of
natural image and medical image computing tasks. However, these achievements
indispensably rely on accurately annotated training data. If encountering some
noisy-labeled images, the network training procedure would suffer from
difficulties, leading to a sub-optimal classifier. This problem is even more
severe in the medical image analysis field, as the annotation quality of
medical images heavily relies on the expertise and experience of annotators. In
this paper, we propose a novel collaborative training paradigm with global and
local representation learning for robust medical image classification from
noisy-labeled data to combat the lack of high quality annotated medical data.
Specifically, we employ the self-ensemble model with a noisy label filter to
efficiently select the clean and noisy samples. Then, the clean samples are
trained by a collaborative training strategy to eliminate the disturbance from
imperfect labeled samples. Notably, we further design a novel global and local
representation learning scheme to implicitly regularize the networks to utilize
noisy samples in a self-supervised manner. We evaluated our proposed robust
learning strategy on four public medical image classification datasets with
three types of label noise,ie,random noise, computer-generated label noise, and
inter-observer variability noise. Our method outperforms other learning from
noisy label methods and we also conducted extensive experiments to analyze each
component of our method.
