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DTAN: Diffusion-based Text Attention Network for medical image segmentation.

Yiyang Zhao1, Jinjiang Li1, Lu Ren2

  • 1School of Information and electronic engineering, Shandong Technology and Business University, Yantai, China.

Computers in Biology and Medicine
|November 20, 2023
PubMed
Summary

The new Diffusion Text-Attention Network (DTAN) improves medical image segmentation by combining text attention with diffusion models. This approach enhances accuracy and reduces noise for better results in clinical applications.

Keywords:
Diffusion modelMedical image segmentationText attention mechanism

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computer Vision

Background:

  • Diffusion models are advancing medical image segmentation.
  • Existing methods face challenges with noise and identifying regions of interest.

Purpose of the Study:

  • Introduce the Diffusion Text-Attention Network (DTAN) for enhanced medical image segmentation.
  • Improve segmentation precision and integrity by integrating text attention and diffusion models.

Main Methods:

  • DTAN utilizes a text attention mechanism to focus on significant regions.
  • A diffusion model is integrated to reduce noise and background interference.
  • The framework was evaluated on Kvasir-Sessile, Kvasir-SEG, and GlaS datasets.

Main Results:

  • DTAN demonstrated significant improvements on the Kvasir-Sessile dataset.
  • Achieved a 2.77% increase in mean Intersection over Union (mIoU).
  • Achieved a 3.06% increase in mean Dice Similarity Coefficient (mDSC).

Conclusions:

  • DTAN shows generalizability and robustness in medical image segmentation.
  • The proposed method offers distinct advantages over state-of-the-art techniques.
  • DTAN holds promise for clinical applications in medical image analysis.