Parotid gland tumor is a common type of head and neck tumor. Segmentation of
the parotid glands and tumors by MR images is important for the treatment of
parotid gland tumors. However, segmentation of the parotid glands is
particularly challenging due to their variable shape and low contrast with
surrounding structures. Recently deep learning has developed rapidly, which can
handle complex problems. However, most of the current deep learning methods for
processing medical images are still based on supervised learning. Compared with
natural images, medical images are difficult to acquire and costly to label.
Contrastive learning, as an unsupervised learning method, can more effectively
utilize unlabeled medical images. In this paper, we used a Transformer-based
contrastive learning method and innovatively trained the contrastive learning
network with transfer learning. Then, the output model was transferred to the
downstream parotid segmentation task, which improved the performance of the
parotid segmentation model on the test set. The improved DSC was 89.60%, MPA
was 99.36%, MIoU was 85.11%, and HD was 2.98. All four metrics showed
significant improvement compared to the results of using a supervised learning
model as a pre-trained model for the parotid segmentation network. In addition,
we found that the improvement of the segmentation network by the contrastive
learning model was mainly in the encoder part, so this paper also tried to
build a contrastive learning network for the decoder part and discussed the
problems encountered in the process of building.

本文使用基于 Transformer 的对比学习方法和迁移学习训练模型，通过创新性的方法提高了腮腺分割模型的性能，并比较了有监督学习模型与对比学习模型的表现差异。