  Transformer, a model comprising attention-based encoder-decoder architecture,
have gained prevalence in the field of natural language processing (NLP) and
recently influenced the computer vision (CV) space. The similarities between
computer vision and medical imaging, reviewed the question among researchers if
the impact of transformers on computer vision be translated to medical imaging?
In this paper, we attempt to provide a comprehensive and recent review on the
application of transformers in medical imaging by; describing the transformer
model comparing it with a diversity of convolutional neural networks (CNNs),
detailing the transformer based approaches for medical image classification,
segmentation, registration and reconstruction with a focus on the image
modality, comparing the performance of state-of-the-art transformer
architectures to best performing CNNs on standard medical datasets.
