  In this work, we used a semi-supervised learning method to train deep
learning model that can segment the brain MRI images. The semi-supervised model
uses less labeled data, and the performance is competitive with the supervised
model with full labeled data. This framework could reduce the cost of labeling
MRI images. We also introduced robust loss to reduce the noise effects of
inaccurate labels generated in semi-supervised learning.

Image and Video Processing;Computer Vision and Pattern Recognition