The Transformer network is the main application area for natural language processing. It has gained traction lately and exhibits potential in the field of computer vision. This cutting-edge method has proven to offer a significant impact on image analysis, a crucial area of computer vision. The transformer's outstanding performance in vision computing places it as an alternative to the convolutional neural network for vision tasks. Transformers have taken center stage in the field of natural language processing. Despite the outstanding performance of transformer networks in natural image processing, their implementation in medical image analysis is gradually gaining roots. This study focuses on the transformer application in natural and medical image analysis. The first part of the study provides an overview of the core concepts of the attention mechanism built into transformers for long-range feature extraction. The study again highlights the various transformer architectures proposed for natural and medical image tasks such as segmentation, classification, image registration and diagnosis. Finally, the paper presents limitations identified in proposed transformer networks for natural and medical image processing. It also highlights prospective study opportunities for further research to better the computer vision domain, especially medical image analysis. This study offers knowledge to scholars and researchers studying computer vision applications as they focus on creating innovative transformer network-based solutions.

中文翻译：

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用于自然和医学图像分析的变压器网络的全面综述


Transformer网络是自然语言处理的主要应用领域。它最近受到关注并在计算机视觉领域展现出潜力。事实证明，这种尖端方法对图像分析（计算机视觉的一个关键领域）具有重大影响。 Transformer 在视觉计算方面的出色表现使其成为视觉任务中卷积神经网络的替代方案。 Transformer 已成为自然语言处理领域的中心舞台。尽管 Transformer 网络在自然图像处理中表现出色，但其在医学图像分析中的应用也逐渐深入人心。本研究重点关注变压器在自然和医学图像分析中的应用。研究的第一部分概述了用于远程特征提取的 Transformer 中内置的注意力机制的核心概念。该研究再次强调了为自然和医学图像任务（例如分割、分类、图像配准和诊断）提出的各种 Transformer 架构。最后，本文提出了所提出的用于自然和医学图像处理的变压器网络中发现的局限性。它还强调了进一步研究的前瞻性研究机会，以改善计算机视觉领域，特别是医学图像分析。这项研究为研究计算机视觉应用的学者和研究人员提供了知识，因为他们专注于创建基于变压器网络的创新解决方案。