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

Abstract

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

CLC Number:

TP391.4

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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References 14

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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

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Detection method of medical cell image generation based on conditional generative adversarial network

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