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：在标准医院血液检测中，传统流程需要医生通过显微镜从患者血液显微图像中手动分离白细胞。这些分离后的白细胞经过自动分类器进行分类，用以确定血液样本中各类白细胞的比例与体积，从而辅助疾病诊断。该方法不仅耗时费力，而且易受图像质量、环境条件等因素影响导致较高错误率，可能引发后续分类错误及误诊。针对上述问题，本文提出一种创新的白细胞检测方法：多层级特征融合与可变形自注意力DETR（MFDS-DETR）。为应对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）实现多层级融合。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，再将筛选后的信息与高层特征融合，从而增强模型特征表达能力。进一步，我们通过编码器中引入多尺度可变形自注意力模块，并利用解码器的自注意力与交叉可变形注意力机制，解决白细胞特征稀疏问题，有助于提取白细胞特征图的全局特征。通过在私有WBCDD、公开LISC和BCCD数据集上与其他前沿白细胞检测模型的对比，验证了所提MFDS-DETR方法的有效性、优越性与泛化能力。我们的源代码与私有WBCDD数据集可在https://github.com/JustlfC03/MFDS-DETR获取。