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Transformer, the latest technological advance of deep learning, has gainedprevalence in natural language processing or computer vision. Since medicalimaging bear some resemblance to computer vision, it is natural to inquireabout the status quo of Transformers in medical imaging and ask the question:can the Transformer models transform medical imaging? In this paper, we attemptto make a response to the inquiry. After a brief introduction of thefundamentals of Transformers, especially in comparison with convolutionalneural networks (CNNs), and highlighting key defining properties thatcharacterize the Transformers, we offer a comprehensive review of thestate-of-the-art Transformer-based approaches for medical imaging and exhibitcurrent research progresses made in the areas of medical image segmentation,recognition, detection, registration, reconstruction, enhancement, etc. Inparticular, what distinguishes our review lies in its organization based on theTransformer's key defining properties, which are mostly derived from comparingthe Transformer and CNN, and its type of architecture, which specifies themanner in which the Transformer and CNN are combined, all helping the readersto best understand the rationale behind the reviewed approaches. We concludewith discussions of future perspectives.

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives