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Related Experiment Video

Updated: Jul 21, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Recent progress in transformer-based medical image analysis.

Zhaoshan Liu¹, Qiujie Lv², Ziduo Yang²

¹Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 117575, Singapore.

Computers in Biology and Medicine

|July 26, 2023

Summary

This summary is machine-generated.

Transformers, initially for natural language processing, show great promise in computer vision (CV) for medical image analysis (MIA). This review details their applications and superior performance across various MIA tasks.

Keywords:

Attention mechanism Convolutional neural network Deep learning Medical image analysis Transformer

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Area of Science:

Computer Vision
Medical Image Analysis
Artificial Intelligence

Background:

Transformers, originating from natural language processing, are increasingly adopted in computer vision.
Medical Image Analysis (MIA) is a critical field within computer vision that can significantly benefit from transformer technology.

Purpose of the Study:

To review the recent advancements and applications of transformers in medical image analysis.
To provide a comprehensive overview of transformer architectures and their core attention mechanisms relevant to MIA.
To discuss the performance of transformer-based methods compared to existing approaches in various MIA tasks.

Main Methods:

Recap of the transformer's attention mechanism and detailed structures.
Systematic organization of transformer applications in MIA across tasks like classification, segmentation, and synthesis.
Analysis of transformer performance across eleven medical image modalities for classification and segmentation.

Main Results:

Transformer-based methods demonstrate superior performance over existing techniques in multiple MIA tasks.
Experiments show significant improvements across various evaluation metrics for transformer applications in MIA.
The review covers a wide range of applications including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis.

Conclusions:

Transformers represent a state-of-the-art technique with significant potential to advance medical image analysis.
Open challenges and future opportunities exist for further development and application of transformers in MIA.
This task-modality review offers valuable insights for the broader MIA community.