Due to the contradiction of medical image processing, that is, the
application of medical images is more and more widely and the limitation of
medical images is difficult to label, few-shot learning technology has begun to
receive more attention in the field of medical image processing. This paper
proposes a Cross-Reference Transformer for medical image segmentation, which
addresses the lack of interaction between the existing Cross-Reference support
image and the query image. It can better 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.Comment: 6 pages,4 figure