  Transformer, the latest technological advance of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical
imaging bear some resemblance to computer vision, it is natural to inquire
about the status quo of Transformers in medical imaging and ask the question:
can the Transformer models transform medical imaging? In this paper, we attempt
to make a response to the inquiry. After a brief introduction of the
fundamentals of Transformers, especially in comparison with convolutional
neural networks (CNNs), and highlighting key defining properties that
characterize the Transformers, we offer a comprehensive review of the
state-of-the-art Transformer-based approaches for medical imaging and exhibit
current research progresses made in the areas of medical image segmentation,
recognition, detection, registration, reconstruction, enhancement, etc. In
particular, what distinguishes our review lies in its organization based on the
Transformer's key defining properties, which are mostly derived from comparing
the Transformer and CNN, and its type of architecture, which specifies the
manner in which the Transformer and CNN are combined, all helping the readers
to best understand the rationale behind the reviewed approaches. We conclude
with discussions of future perspectives.
