Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis.
@article{arxiv.2202.12165,
title = {Transformers in Medical Image Analysis: A Review},
author = {Kelei He and Chen Gan and Zhuoyuan Li and Islem Rekik and Zihao Yin and Wen Ji and Yang Gao and Qian Wang and Junfeng Zhang and Dinggang Shen},
journal= {arXiv preprint arXiv:2202.12165},
year = {2022}
}