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