Transformers in Medical Image Analysis: A Review
- URL: http://arxiv.org/abs/2202.12165v1
- Date: Thu, 24 Feb 2022 16:04:03 GMT
- Title: Transformers in Medical Image Analysis: A Review
- Authors: Kelei He, Chen Gan, Zhuoyuan Li, Islem Rekik, Zihao Yin, Wen Ji, Yang
Gao, Qian Wang, Junfeng Zhang, and Dinggang Shen
- Abstract summary: Our paper presents both a position paper and a primer, promoting 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 give a new taxonomy of various Transformer architectures tailored for medical image applications and discuss their limitations.
- Score: 46.71636151229035
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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 presents both a position
paper and a primer, promoting 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 give a new taxonomy of 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.
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