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<p>White blood cell (WBC) detection is pivotal in medical diagnostics, crucial for diagnosing infections, inflammations, and certain cancers. Traditional WBC detection methods are labor-intensive and time-consuming. Convolutional Neural Networks (CNNs) are widely used for cell detection due to their strong feature extraction capability. However, they struggle with global information and long-distance dependencies in WBC images. Transformers, on the other hand, excel at modeling long-range dependencies, which improves their performance in vision tasks. To tackle the large foreground-background differences in WBC images, this paper introduces a novel WBC detection method, named the Multi-Scale Cross-Deformation Attention Fusion Network (MCDAF-Net), which combines CNNs and Transformers. The Attention Multi-scale Sensing Module (AMSM) is designed to localize WBCs more accurately by fusing features at different scales and enhancing feature representation through a self-attention mechanism. The Cross-Deformation Convolution Module (CDCM) reduces feature correlation, aiding the model in capturing diverse aspects and patterns in images, thereby improving generalization. MCDAF-Net outperforms other models on public datasets (LISC, BCCD, and WBCDD), demonstrating its superiority in WBC detection. Our code and pretrained models: <a href="https://github.com/xqq777/MCDAF-Net">https://github.com/xqq777/MCDAF-Net</a>.</p>

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Multiscale deformed attention networks for white blood cell detection

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月26日

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  • Xin Zheng,
  • Qiqi Xu,
  • Shiyi Zheng,
  • Luxian Zhao,
  • Deyang Liu,
  • Liangliang Zhang

摘要

White blood cell (WBC) detection is pivotal in medical diagnostics, crucial for diagnosing infections, inflammations, and certain cancers. Traditional WBC detection methods are labor-intensive and time-consuming. Convolutional Neural Networks (CNNs) are widely used for cell detection due to their strong feature extraction capability. However, they struggle with global information and long-distance dependencies in WBC images. Transformers, on the other hand, excel at modeling long-range dependencies, which improves their performance in vision tasks. To tackle the large foreground-background differences in WBC images, this paper introduces a novel WBC detection method, named the Multi-Scale Cross-Deformation Attention Fusion Network (MCDAF-Net), which combines CNNs and Transformers. The Attention Multi-scale Sensing Module (AMSM) is designed to localize WBCs more accurately by fusing features at different scales and enhancing feature representation through a self-attention mechanism. The Cross-Deformation Convolution Module (CDCM) reduces feature correlation, aiding the model in capturing diverse aspects and patterns in images, thereby improving generalization. MCDAF-Net outperforms other models on public datasets (LISC, BCCD, and WBCDD), demonstrating its superiority in WBC detection. Our code and pretrained models: https://github.com/xqq777/MCDAF-Net.