  Transformers have dominated the field of natural language processing, and
recently impacted the computer vision area. In the field of medical image
analysis, Transformers have also been successfully applied to full-stack
clinical applications, including image synthesis/reconstruction, registration,
segmentation, detection, and diagnosis. Our paper aims to promote awareness and
application of Transformers in the field of medical image analysis.
Specifically, we first overview the core concepts of the attention mechanism
built into Transformers and other basic components. Second, we review various
Transformer architectures tailored for medical image applications and discuss
their limitations. Within this review, we investigate key challenges revolving
around the use of Transformers in different learning paradigms, improving the
model efficiency, and their coupling with other techniques. We hope this review
can give a comprehensive picture of Transformers to the readers in the field of
medical image analysis.
