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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Next-Gen Medical Imaging: U-Net Evolution and the Rise of Transformers.

Chen Zhang¹, Xiangyao Deng¹, Sai Ho Ling¹

¹School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Sensors (Basel, Switzerland)

|July 27, 2024

Summary

This summary is machine-generated.

This review compares U-Net and Transformer-based deep learning models for medical imaging. Transformer models show revolutionary potential for overcoming challenges like low contrast and noise in medical image analysis.

Keywords:

CT scan Transformer-based models X-ray deep learning high resolution medical imaging segmentation medical sensing noisy level sensitivity ultrasound device

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Area of Science:

Medical Imaging
Deep Learning
Artificial Intelligence in Healthcare

Background:

Medical imaging advancements have improved disease understanding but face challenges like low contrast, high noise, and limited resolution.
The U-Net architecture is widely used in medical imaging for its effectiveness, with numerous variants addressing specific issues.

Purpose of the Study:

To provide a comparative analysis of U-Net and emerging Transformer-based models in medical imaging.
To examine the evolution, limitations, and potential of deep learning architectures for medical image analysis.

Main Methods:

Review of U-Net architecture and its variants.
Introduction to Transformer-based self-attention mechanisms and positional information incorporation.
Analysis of recent Transformer models in medical imaging.

Main Results:

U-Net architecture has evolved but still faces limitations in medical imaging challenges.
Transformer-based models represent a new era, demonstrating significant potential for medical image analysis.
Comparative analysis highlights the strengths and weaknesses of both architectures.

Conclusions:

Transformer-based models offer revolutionary potential for advancing medical imaging by addressing persistent challenges.
Further research is needed to fully explore the capabilities and limitations of Transformer techniques in this field.