LETCP: A Label-Efficient Transformer-Based Contrastive Pre-Training Method for Brain Tumor Segmentation

Abstract

The manual annotation of brain tumor images is costly and relies heavily on physician expertise, which limits the implementation of automated and accurate brain tumor segmentation in clinical practice. Meanwhile, unlabeled images are readily available but not well-exploited. In this paper, a novel brain tumor segmentation method for improving the efficiency of labeled images is proposed, dubbed LETCP. Specifically, it presents a contrastive pre-training strategy that utilises unlabeled data for model pre-training. The segmentation model in this approach is constructed based on a self-attention transformer. Extensive evaluation of the method is performed on three public datasets. By using pre-training with unlabeled data and then fine-tuning with small amounts of labeled data, this method achieves segmentation performance surpassing other semi-supervised methods and shows competitive performance compared with supervised methods.