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<p>In the evolving field of medical image segmentation, the Diffusion U-Net Coupled with Text-Attention Guided Block for Medical Image Segmentation (DUT) network marks a significant advancement. This innovative network integrates text attention mechanisms, diffusion models, and multi-scale fusion strategies to enhance precision and detail in segmenting medical images. DUT’s text attention mechanisms focus on key areas, improving target region recognition and segmentation accuracy. The network’s diffusion model reduces noise and irrelevant background, refining image quality and capturing target details more precisely. Additionally, DUT’s multi-scale fusion module processes features across various scales, enhancing adaptability to complex image structures and improving recognition of both small and large targets. Extensive testing on four datasets—Kvasir-Sessile, Kvasir-SEG, PH2, and DSB2018, demonstrate DUT’s superior performance, particularly on the clinically relevant Kvasir-Sessile dataset. DUT outperformed leading technologies, showing a 4.24% improvement in the mIoU metric and a 3.22% enhancement in the mDSC metric. These results highlight DUT’s broad applicability and robustness in medical image segmentation, confirming its significant advantages and potential impact in the field <a href="https://github.com/cn-xvkong/DUT.">https://github.com/cn-xvkong/DUT.</a></p>

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Diffusion U-Net coupled with text-attention guided block for medical image segmentation

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Jianjun Li,
  • Chengming Wang,
  • Peng Duan,
  • Jinjiang Li

摘要

In the evolving field of medical image segmentation, the Diffusion U-Net Coupled with Text-Attention Guided Block for Medical Image Segmentation (DUT) network marks a significant advancement. This innovative network integrates text attention mechanisms, diffusion models, and multi-scale fusion strategies to enhance precision and detail in segmenting medical images. DUT’s text attention mechanisms focus on key areas, improving target region recognition and segmentation accuracy. The network’s diffusion model reduces noise and irrelevant background, refining image quality and capturing target details more precisely. Additionally, DUT’s multi-scale fusion module processes features across various scales, enhancing adaptability to complex image structures and improving recognition of both small and large targets. Extensive testing on four datasets—Kvasir-Sessile, Kvasir-SEG, PH2, and DSB2018, demonstrate DUT’s superior performance, particularly on the clinically relevant Kvasir-Sessile dataset. DUT outperformed leading technologies, showing a 4.24% improvement in the mIoU metric and a 3.22% enhancement in the mDSC metric. These results highlight DUT’s broad applicability and robustness in medical image segmentation, confirming its significant advantages and potential impact in the field https://github.com/cn-xvkong/DUT.