Multiscale deformed attention networks for white blood cell detection

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 .