Vision Transformers in Medical Imaging: A Review

TL;DR

This review paper explores the application of transformer models in medical imaging, comparing their architecture and performance to CNNs across various tasks and modalities, highlighting recent advancements and potential impacts.

Contribution

It provides a comprehensive overview of transformer-based methods in medical imaging, including comparisons with CNNs and analysis of their effectiveness across different applications.

Findings

Transformers show competitive performance with CNNs in medical image tasks.

Recent transformer architectures outperform traditional methods on standard datasets.

The review highlights potential for transformers to advance medical imaging analysis.

Abstract

Transformer, a model comprising attention-based encoder-decoder architecture, have gained prevalence in the field of natural language processing (NLP) and recently influenced the computer vision (CV) space. The similarities between computer vision and medical imaging, reviewed the question among researchers if the impact of transformers on computer vision be translated to medical imaging? In this paper, we attempt to provide a comprehensive and recent review on the application of transformers in medical imaging by; describing the transformer model comparing it with a diversity of convolutional neural networks (CNNs), detailing the transformer based approaches for medical image classification, segmentation, registration and reconstruction with a focus on the image modality, comparing the performance of state-of-the-art transformer architectures to best performing CNNs on standard medical datasets.