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Deep learning models have become the mainstream method for medical imagesegmentation, but they require a large manually labeled dataset for trainingand are difficult to extend to unseen categories. Few-shot segmentation(FSS)has the potential to address these challenges by learning new categories from asmall number of labeled samples. The majority of the current methods employ aprototype learning architecture, which involves expanding support prototypevectors and concatenating them with query features to conduct conditionalsegmentation. However, such framework potentially focuses more on queryfeatures while may neglect the correlation between support and query features.In this paper, we propose a novel self-supervised few shot medical imagesegmentation network with Cross-Reference Transformer, which addresses the lackof interaction between the support image and the query image. We first enhancethe correlation features between the support set image and the query imageusing a bidirectional cross-attention module. Then, we employ a cross-referencemechanism to mine and enhance the similar parts of support features and queryfeatures in high-dimensional channels. Experimental results show that theproposed model achieves good results on both CT dataset and MRI dataset.

Few-shot Medical Image Segmentation via Cross-Reference Transformer