论文标题
半监督学习，在脑部分割中有强大的损失
Semi-supervised Learning with Robust Loss in Brain Segmentation
论文作者
论文摘要
在这项工作中，我们使用了一种半监督的学习方法来训练可以分割大脑MRI图像的深度学习模型。半监督模型使用标记的数据较少，并且性能与带有完整标记数据的监督模型具有竞争力。该框架可以降低标记MRI图像的成本。我们还引入了强大的损失，以减少半监督学习中产生的不准确标签的噪声效应。
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.
