Scholarlink

<p>With the rapid growth of medical imaging data, precise segmentation and analysis of medical images face unprecedented challenges. Addressing small sample sizes, significant variations, and structurally complex medical imaging data to improve the accuracy of early diagnosis has become a key issue in the medical field. This study proposes a Residual U-KAN model (ResU-KAN) to tackle this challenge and improve medical image segmentation accuracy. First, to address the model’s shortcomings in capturing long-distance dependencies and issues like potential gradient vanishing (or explosion) and overfitting, we introduce a Residual Convolution Attention (RCA) module. Second, to expand the model’s receptive field while performing multi-scale feature extraction, we introduce an Atrous Spatial Pyramid Pooling module (ASPP). Finally, experiments were conducted on three publicly available medical imaging datasets, and comparative analysis with existing state-of-the-art methods demonstrated the effectiveness of the proposed approach. Project page: <a href="https://github.com/Alfreda12/ResU-KAN">https://github.com/Alfreda12/ResU-KAN</a></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ResU-KAN: a medical image segmentation model integrating residual convolutional attention and atrous spatial pyramid pooling

  • 期刊论文

  • 发表日期：

    2025年3月25日

查看全文
  • Haibin Wang,
  • Zhenfeng Zhao,
  • Qi Liu,
  • Shenwen Wang

摘要

With the rapid growth of medical imaging data, precise segmentation and analysis of medical images face unprecedented challenges. Addressing small sample sizes, significant variations, and structurally complex medical imaging data to improve the accuracy of early diagnosis has become a key issue in the medical field. This study proposes a Residual U-KAN model (ResU-KAN) to tackle this challenge and improve medical image segmentation accuracy. First, to address the model’s shortcomings in capturing long-distance dependencies and issues like potential gradient vanishing (or explosion) and overfitting, we introduce a Residual Convolution Attention (RCA) module. Second, to expand the model’s receptive field while performing multi-scale feature extraction, we introduce an Atrous Spatial Pyramid Pooling module (ASPP). Finally, experiments were conducted on three publicly available medical imaging datasets, and comparative analysis with existing state-of-the-art methods demonstrated the effectiveness of the proposed approach. Project page: https://github.com/Alfreda12/ResU-KAN