  The remarkable performance of the Transformer architecture in natural
language processing has recently also triggered broad interest in Computer
Vision. Among other merits, Transformers are witnessed as capable of learning
long-range dependencies and spatial correlations, which is a clear advantage
over convolutional neural networks (CNNs), which have been the de facto
standard in Computer Vision problems so far. Thus, Transformers have become an
integral part of modern medical image analysis. In this review, we provide an
encyclopedic review of the applications of Transformers in medical imaging.
Specifically, we present a systematic and thorough review of relevant recent
Transformer literature for different medical image analysis tasks, including
classification, segmentation, detection, registration, synthesis, and clinical
report generation. For each of these applications, we investigate the novelty,
strengths and weaknesses of the different proposed strategies and develop
taxonomies highlighting key properties and contributions. Further, if
applicable, we outline current benchmarks on different datasets. Finally, we
summarize key challenges and discuss different future research directions. In
addition, we have provided cited papers with their corresponding
implementations in https://github.com/mindflow-institue/Awesome-Transformer.
