  Deep neural networks have achieved satisfactory performance in piles of
medical image analysis tasks. However the training of deep neural network
requires a large amount of samples with high-quality annotations. In medical
image segmentation, it is very laborious and expensive to acquire precise
pixel-level annotations. Aiming at training deep segmentation models on
datasets with probably corrupted annotations, we propose a novel Meta Corrupted
Pixels Mining (MCPM) method based on a simple meta mask network. Our method is
targeted at automatically estimate a weighting map to evaluate the importance
of every pixel in the learning of segmentation network. The meta mask network
which regards the loss value map of the predicted segmentation results as
input, is capable of identifying out corrupted layers and allocating small
weights to them. An alternative algorithm is adopted to train the segmentation
network and the meta mask network, simultaneously. Extensive experimental
results on LIDC-IDRI and LiTS datasets show that our method outperforms
state-of-the-art approaches which are devised for coping with corrupted
annotations.
