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Abstract：
The development of various models for automated images screening has significantly enhanced the efficiency and accuracy of cervical cytology image analysis. Single-stage target detection models are capable of fast detection of abnormalities in cervical cytology, but an accurate diagnosis of abnormal cells not only relies on identification of a single cell itself, but also involves the comparison with the surrounding cells. Herein we present the Trans-YOLOv5 model, an automated abnormal cell detection model based on the YOLOv5 model incorporating the global-local attention mechanism to allow efficient multiclassification detection of abnormal cells in cervical cytology images. The experimental results using a large cervical cytology image dataset demonstrated the efficiency and accuracy of this model in comparison with the state-of-the-art methods, with a mAP reaching 65.9% and an AR reaching 53.3%, showing a great potential of this model in automated cervical cancer screening based on cervical cytology images.
摘要：
用于自动图像筛选的各种模型的发展显著提高了宫颈细胞学图像分析的效率和准确性。单阶段目标检测模型能够快速检测宫颈细胞学异常，但是异常细胞的准确诊断不仅依赖于单个细胞本身的识别，但也涉及与周围细胞的比较。在这里，我们介绍了Trans-YOLOv5模型，基于YOLOv5模型的自动化异常细胞检测模型,结合了全局-局部注意机制,可以对宫颈细胞学图像中的异常细胞进行有效的多分类检测.使用大型宫颈细胞学图像数据集的实验结果表明，与最先进的方法相比，该模型的效率和准确性。MAP达到65.9%，AR达到53.3%，该模型在基于宫颈细胞学图像的自动宫颈癌筛查中显示出巨大的潜力。
