Li Gang, Li Xingguang, Wang Yuting, Gong Shu, Yang Yanting, Xu Chuanyun
School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, China.
Department of Gastroenterology, Children's Hospital of Chongqing Medical University, Chongqing 400014, China.
Bioengineering (Basel). 2024 Jul 5;11(7):686. doi: 10.3390/bioengineering11070686.
Automated detection of cervical lesion cell/clumps in cervical cytological images is essential for computer-aided diagnosis. In this task, the shape and size of the lesion cell/clumps appeared to vary considerably, reducing the detection performance of cervical lesion cell/clumps. To address the issue, we propose an adaptive feature extraction network for cervical lesion cell/clumps detection, called AFE-Net. Specifically, we propose the adaptive module to acquire the features of cervical lesion cell/clumps, while introducing the global bias mechanism to acquire the global average information, aiming at combining the adaptive features with the global information to improve the representation of the target features in the model, and thus enhance the detection performance of the model. Furthermore, we analyze the results of the popular bounding box loss on the model and propose the new bounding box loss tendency-IoU (TIoU). Finally, the network achieves the mean Average Precision (mAP) of 64.8% on the CDetector dataset, with 30.7 million parameters. Compared with YOLOv7 of 62.6% and 34.8M, the model improved mAP by 2.2% and reduced the number of parameters by 11.8%.
在宫颈细胞学图像中自动检测宫颈病变细胞/细胞团对于计算机辅助诊断至关重要。在这项任务中，病变细胞/细胞团的形状和大小差异很大，这降低了宫颈病变细胞/细胞团的检测性能。为了解决这个问题，我们提出了一种用于宫颈病变细胞/细胞团检测的自适应特征提取网络，称为AFE-Net。具体来说，我们提出了自适应模块来获取宫颈病变细胞/细胞团的特征，同时引入全局偏差机制来获取全局平均信息，旨在将自适应特征与全局信息相结合，以改善模型中目标特征的表示，从而提高模型的检测性能。此外，我们分析了模型上流行的边界框损失结果，并提出了新的边界框损失趋势交并比（TIoU）。最后，该网络在CDetector数据集上实现了64.8%的平均精度均值（mAP），有3070万个参数。与YOLOv7的62.6%和3480万个参数相比，该模型将mAP提高了2.2%，并将参数数量减少了11.8%。
