基于条件生成对抗网络的医学细胞图像生成检测方法

doi:10.13229/j.cnki.jdxbgxb20200204

摘要/Abstract

摘要：

针对现有医学细胞图像生成检测方法在检测中需要大量的有标签数据作为支撑，而细胞在黏附遮挡的情况下数据匮乏，不利于细胞检测精度的提高这一问题，提出了基于条件生成对抗网络的细胞图像生成检测方法。通过Pix2pix网络模型控制生成黏附遮挡的细胞图像，提取损失函数，采用Pix2pix实现图像到图像的转换，并运用正则项误差控制生成对抗网络误差。在此基础上，构建检测网络，包括生成网络结构、判别网络结构和检测网络结构，在生成网络输出端进行目标检测，使图像生成与细胞检测工作在同一个网络中完成。实验表明，与现有模型相比，本文方法在检测精度上有显著提升，达到了90.2%，可以满足医学细胞检测需求。

关键词: 条件生成对抗网络, 图像生成, 目标检测, 判别网络结构, 损失函数

Abstract:

The existing methods need a lot of labeled data as support in the detection, but the lack of data in the case of cell adhesion and occlusion is not conducive to the improvement of cell detection accuracy. In order to solve this problem, a cell image generation detection method based on condition generation antagonism network is proposed. Pix2pix network model is used to control the generation of cell image with adhesion occlusion, the loss function is extracted, pix2pix is used to realize image to image conversion, and regular term error control is used to generate network error. On this basis, the detection network is constructed, including the generation network structure, discrimination network structure and detection network structure. The target detection is carried out at the output of the generation network, so that the image generation and cell detection are completed in the same network. Experiments show that compared with the existing model, the designed method has a significant improvement in detection accuracy, reaching 90.2%, which can meet the needs of medical cell detection.

Key words: conditional generative adversarial network, image generation, target detection, discriminant network structure, loss function

中图分类号:

TP391.4

陈雪云,许韬,黄小巧. 基于条件生成对抗网络的医学细胞图像生成检测方法[J]. 吉林大学学报(工学版), 2021, 51(4): 1414-1419.

Xue-yun CHEN,Tao XU,Xiao-qiao HUANG. Detection method of medical cell image generation based on conditional generative adversarial network[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1414-1419.

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方法名称	迭代次数	PSRN	SSIM	IS
Pix2pix	50 000	17.54	0.843	0.79
LCGAN	50 000	20.87	0.905	0.82

方法名称	迭代次数	召回率/%	精确率/%	准确率
BP+HOG	10 000	68.4	85.8	0.77
SSD	10 000	78.3	89.2	0.83
FCRN	10 000	90.3	91.2	0.894
LCGAN	10 000	91.2	91.7	0.902