In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients' blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model's feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD dataset are available at https://github.com/JustlfC03/MFDS-DETR.

翻译：在医院常规血液检测中，传统流程要求医生通过显微镜从患者血液显微图像中手动分离白细胞，继而使用自动白细胞分类器对分离后的白细胞进行类别划分，以确定血液样本中不同类型白细胞的比例与体积，辅助疾病诊断。该方法不仅耗时费力，且因图像质量与环境条件等因素具有较高错误倾向，可能导致后续错误分类与误诊。针对上述问题，本文提出一种创新的白细胞检测方法：多层特征融合与可变形自注意力DETR（MFDS-DETR）。为解决白细胞尺度差异问题，我们设计了高层筛选特征融合金字塔（HS-FPN），实现多层特征融合。该模型以高层特征作为权重，通过通道注意力模块筛选低层特征信息，并将筛选后的信息与高层特征融合，从而增强模型的特征表达能力。进一步地，针对白细胞特征稀疏问题，我们在编码器中引入多尺度可变形自注意力模块，并利用解码器中的自注意力机制与交叉可变形注意力机制，有效提取白细胞特征图的全局特征。通过与基于私有WBCDD数据集、公开LISC与BCCD数据集的其他前沿白细胞检测模型进行对比，我们所提出的MFDS-DETR方法的有效性、优越性与泛化性得到充分验证。源代码与私有WBCCD数据集已开源至https://github.com/JustlfC03/MFDS-DETR。