HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou, 310018, China.
Medical Big Data Lab, Shenzhen Research Institute of Big Data, Shenzhen, 518000, China.
Comput Biol Med. 2024 Mar;170:107917. doi: 10.1016/j.compbiomed.2024.107917. Epub 2024 Jan 6.
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. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. 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.
在标准的医院血液测试中，传统的过程需要医生使用显微镜手动从患者血液的显微镜图像中分离白细胞。然后，这些分离的白细胞通过自动白细胞分类器进行分类，以确定血液样本中不同类型白细胞的比例和体积，从而辅助疾病诊断。这种方法不仅耗时耗力，而且由于图像质量和环境条件等因素，容易出现错误，这可能导致后续分类错误和误诊。当代白细胞检测方法在处理白细胞特征较少的图像和不同白细胞之间的比例差异方面存在局限性，在大多数情况下结果都不尽如人意。为了解决这些问题，本文提出了一种创新的白细胞检测方法：多层次特征融合和可变形自注意力 DETR（MFDS-DETR）。为了解决白细胞比例差异的问题，我们设计了高层筛选特征融合金字塔（HS-FPN），实现了多层次融合。该模型使用高层特征作为权重，通过通道注意力模块过滤低层特征信息，然后将筛选信息与高层特征融合，从而增强模型的特征表达能力。此外，我们通过在编码器中加入多尺度可变形自注意力模块，并在解码器中使用自注意力和交叉可变形注意力机制，解决了白细胞特征稀缺的问题，有助于提取白细胞特征图的全局特征。通过与其他前沿白细胞检测模型在私有 WBCDD、公共 LISC 和 BCCD 数据集上的比较，验证了所提出的 MFDS-DETR 方法的有效性、优越性和通用性。我们的源代码和私有 WBCCD 数据集可在 https://github.com/JustlfC03/MFDS-DETR 上获得。
