改进YOLOX模型，实现血细胞自动检测，精度与速度双提升
ABCD: Automatic Blood Cell Detection via Attention-Guided Improved YOLOX
- 引入注意力模块和自适应特征融合，增强多尺度特征提取能力
- 在BCCD数据集上达到95.49% [email protected]，较基线提升2.8%
- 适合医疗影像实时分析场景，尤其适用于血细胞自动化诊断
显微镜下血细胞检测已成为医学图像分析的重要方向，对疾病诊断具有关键意义。人工检测耗时长、效率低且易出错。本文提出基于改进YOLOX的自动血细胞检测方法（ABCD），可识别白细胞、红细胞和血小板。首先，在主干网络中引入卷积块注意力模块（CBAM）以提升特征提取效率；其次，在颈部结构中加入自适应空间特征融合（ASFF），优化多阶段特征融合；最后，用完整交并比（CIOU）损失替代交并比（IOU）损失，加速模型收敛。实验结果表明，该方法在BCCD数据集上表现优异：[email protected]达95.49%，较基线提升2.8%；[email protected]达86.89%，提升23.41%；检测速度提高2.9%，适用于实时应用。
原文摘要 · Abstract (English)
Detection of blood cells in microscopic images has become a major focus of medical image analysis, playing a crucial role in gaining valuable insights into a patient's health. Manual blood cell checks for disease detection are known to be time-consuming, inefficient, and error-prone. To address these limitations, analyzing blood cells using deep learning-based object detectors can be regarded as a feasible solution. In this study, we propose automatic blood cell detection method (ABCD) based on an improved version of YOLOX, an object detector, for detecting various types of blood cells, including white blood cells, red blood cells, and platelets. Firstly, we introduce the Convolutional Block Attention Module (CBAM) into the network's backbone to enhance the efficiency of feature extraction. Furthermore, we introduce the Adaptively Spatial Feature Fusion (ASFF) into the network's neck, which optimizes the fusion of different features extracted from various stages of the network. Finally, to speed up the model's convergence, we substitute the Intersection over Union (IOU) loss function with the Complete Intersection over Union (CIOU) loss function. The experimental results demonstrate that the proposed method is more effective than other existing methods for BCCD dataset. Compared to the baseline algorithm, our method ABCD achieved 95.49 % [email protected] and 86.89 % [email protected], which are 2.8% and 23.41% higher, respectively, and increased the detection speed by 2.9%, making it highly efficient for real-time applications.
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