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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Dual-scale shifted window attention network for medical image segmentation.

De-Wei Han1, Xiao-Lei Yin2, Jian Xu1

  • 1School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, 1088 Xueyuan Boulevard, Nanshan District, Shenzhen, 518055, China.

Scientific Reports
|July 31, 2024
PubMed
Summary

This study introduces a Dual-Scale Transformer using double-sized shifted windows to improve image segmentation. This novel approach enhances information flow between image patches, achieving state-of-the-art results.

Keywords:
Dual-scale shifted window attentionMedical image segmentationSwin Transformer

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Image Segmentation

Background:

  • Transformers offer excellent performance in computer vision but can be computationally complex.
  • Window-based and shifted window-based self-attention mechanisms in Swin Transformers aim to reduce this complexity.
  • Optimizing patch information communication is key to improving Transformer efficiency in vision tasks.

Purpose of the Study:

  • To propose a Dual-Scale Transformer with a double-sized shifted window attention method.
  • To investigate the impact of different shifted window sizes on patch information communication efficiency.
  • To enhance image segmentation performance by improving information flow within Transformer networks.

Main Methods:

  • Developed a Dual-Scale Transformer architecture incorporating double-sized shifted window attention.
  • Evaluated the proposed method against CNN-based (U-Net, AttenU-Net, ResU-Net, CE-Net) and single-scale Swin Transformer (SwinT) models.
  • Conducted an ablation study to analyze the effect of shifted window size on information flow and segmentation performance.

Main Results:

  • The Dual-Scale Transformer significantly outperformed CNN-based methods by 3%-6% and single-scale Swin Transformer by approximately 1%.
  • Achieved state-of-the-art segmentation results on Kvasir-SEG, ISIC2017, MICCAI EndoVisSub-Instrument, and CadVesSet datasets.
  • Confirmed that dual-scale shifted window attention enhances patch information communication and segmentation accuracy.

Conclusions:

  • The proposed dual-scale shifted window attention is an optimized network design for image segmentation.
  • This approach effectively improves information flow, leading to enhanced segmentation performance.
  • Network architecture design significantly impacts visual performance in Transformer-based models.