Following unprecedented success on the natural language tasks, Transformers
have been successfully applied to several computer vision problems, achieving
state-of-the-art results and prompting researchers to reconsider the supremacy
of convolutional neural networks (CNNs) as {de facto} operators. Capitalizing
on these advances in computer vision, the medical imaging field has also
witnessed growing interest for Transformers that can capture global context
compared to CNNs with local receptive fields. Inspired from this transition, in
this survey, we attempt to provide a comprehensive review of the applications
of Transformers in medical imaging covering various aspects, ranging from
recently proposed architectural designs to unsolved issues. Specifically, we
survey the use of Transformers in medical image segmentation, detection,
classification, reconstruction, synthesis, registration, clinical report
generation, and other tasks. In particular, for each of these applications, we
develop taxonomy, identify application-specific challenges as well as provide
insights to solve them, and highlight recent trends. Further, we provide a
critical discussion of the field's current state as a whole, including the
identification of key challenges, open problems, and outlining promising future
directions. We hope this survey will ignite further interest in the community
and provide researchers with an up-to-date reference regarding applications of
Transformer models in medical imaging. Finally, to cope with the rapid
development in this field, we intend to regularly update the relevant latest
papers and their open-source implementations at
https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging.

综述了 Transformer 在医学图像中的应用，包括分割、检测、分类、重建、合成、配准和临床报告生成等方面，开发了分类法，确定了应用特定的挑战并提供了解决方案和最近的趋势，以及对整个领域的现状进行了批判性的讨论，包括关键挑战、未解决的问题和有前途的未来方向。