  The superior performance of CNN on medical image analysis heavily depends on
the annotation quality, such as the number of labeled image, the source of
image, and the expert experience. The annotation requires great expertise and
labour. To deal with the high inter-rater variability, the study of imperfect
label has great significance in medical image segmentation tasks. In this
paper, we present a novel cascaded robust learning framework for chest X-ray
segmentation with imperfect annotation. Our model consists of three independent
network, which can effectively learn useful information from the peer networks.
The framework includes two stages. In the first stage, we select the clean
annotated samples via a model committee setting, the networks are trained by
minimizing a segmentation loss using the selected clean samples. In the second
stage, we design a joint optimization framework with label correction to
gradually correct the wrong annotation and improve the network performance. We
conduct experiments on the public chest X-ray image datasets collected by
Shenzhen Hospital. The results show that our methods could achieve a
significant improvement on the accuracy in segmentation tasks compared to the
previous methods.
