Semi-supervised Learning with Robust Loss in Brain Segmentation
This work addresses the problem of high labeling costs in medical imaging for researchers and clinicians, but it is incremental as it builds on existing semi-supervised techniques.
The paper tackled brain MRI segmentation by using a semi-supervised learning method with robust loss to reduce labeling costs and noise effects, achieving performance competitive with fully supervised models.
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.