Pre-training a recognition model with contrastive learning on a large dataset
of unlabeled data has shown great potential to boost the performance of a
downstream task, e.g., image classification. However, in domains such as
medical imaging, collecting unlabeled data can be challenging and expensive. In
this work, we propose to adapt contrastive learning to work with meta-label
annotations, for improving the model's performance in medical image
segmentation even when no additional unlabeled data is available. Meta-labels
such as the location of a 2D slice in a 3D MRI scan or the type of device used,
often come for free during the acquisition process. We use the meta-labels for
pre-training the image encoder as well as to regularize a semi-supervised
training, in which a reduced set of annotated data is used for training.
Finally, to fully exploit the weak annotations, a self-paced learning approach
is used to help the learning and discriminate useful labels from noise. Results
on three different medical image segmentation datasets show that our approach:
i) highly boosts the performance of a model trained on a few scans, ii)
outperforms previous contrastive and semi-supervised approaches, and iii)
reaches close to the performance of a model trained on the full data.

本文提出了一种利用元标签注释的对比学习方法，用于医学影像分割中模型表现的优化。模型在预训练和半监督训练中使用元标签注释，并采用自适应学习方法消除噪声标签，实验结果表明这种方法在几个数据集下表现优异。