Cerebral infarction image recognition based on semi-supervised method

doi:10.11772/j.issn.1001-9081.2020071034

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (4): 1221-1226.DOI: 10.11772/j.issn.1001-9081.2020071034

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles

Cerebral infarction image recognition based on semi-supervised method

OU Lili¹, SHAO Fengjing¹, SUN Rencheng^1,2, SUI Yi¹

1. College of Computer Science and Technology, Qingdao University, Qingdao Shandong 266071, China;
2. The Affiliated Hospital of Qingdao University, Qingdao Shandong 266071, China

Received:2020-07-17 Revised:2020-10-09 Online:2021-04-10 Published:2020-11-12
Supported by:
This work is partially supported by the Youth Program of National Natural Science Foundation of China (41706198).

基于半监督方法的脑梗死图像识别

欧莉莉¹, 邵峰晶¹, 孙仁诚^1,2, 隋毅¹

1. 青岛大学计算机科学技术学院, 山东青岛 266071;
2. 青岛大学附属医院, 山东青岛 266071

通讯作者: 邵峰晶
作者简介:欧莉莉（1993—），女，河南商丘人，硕士研究生，主要研究方向：大数据分析；邵峰晶（1955—），女，山东青州人，教授，博士生导师，博士，主要研究方向：数据挖掘、复杂网络；孙仁诚（1977—），男，山东即墨人，副教授，博士，主要研究方向：数据挖掘、复杂网络；隋毅（1984—），女，山东青岛人，副教授，博士，主要研究方向：大数据建模与分析、复杂网络、机器学习。
基金资助:
国家自然科学基金青年科学基金资助项目（41706198）。

Abstract

Abstract: In the field of image recognition, images with insufficient label data cannot be well recognized by the supervised method model. In order to solve this problem, a semi-supervised method model based on Generative Adversarial Network(GAN) was proposed. That is, by combining the advantages of semi-supervised GANs and deep convolutional GANs, and replacing the sigmoid activation function with softmax in the output layer, the Semi-Supervised Deep Convolutional GAN(SS-DCGAN) model was established. Firstly, the generated samples were defined as pseudo-samples and used to guide the training process. Secondly, the semi-supervised training method was adopted to update the parameters of the model. Finally, the recognition of abnormal(cerebral infarction) images was realized. Experimental results show that the SS-DCGAN model can recognize abnormal images well with little label data, which achieves 95.05% recognition rates. Compared with Residual Network 32(ResNet32) and Ladder networks, the SS-DCGAN model has significant advantages.

Key words: Generative Adversarial Network (GAN), semi-supervised, cerebral infarction, deep convolutional networks, image recognition, feature matching

摘要： 在图像识别领域，针对有监督方法的模型在标签数据不足时图像的识别效果不佳问题，提出一种基于生成对抗网络（GAN）的半监督方法模型，即结合了半监督生成对抗网络（SSGAN）和深度卷积生成对抗网络（DCGAN）的优点，并在输出层用softmax代替了sigmoid激活函数，从而建立半监督深度卷积生成对抗网络（SS-DCGAN）模型。首先，将生成样本定义为伪样本类别并用于引导训练；其次，采用半监督的训练方式对模型的参数进行更新；最后，实现对异常（脑梗死）图像的识别。实验结果表明，SS-DCGAN模型在标签数据较少时能够很好地识别异常图像，达到95.05%的识别率，与ResNet32、半监督梯度网络（Ladder Network）分类方法相比具有显著的优越性。

关键词: 生成对抗网络, 半监督, 脑梗, 深度卷积网络, 图像识别, 特征匹配

CLC Number:

TP391.41

OU Lili, SHAO Fengjing, SUN Rencheng, SUI Yi. Cerebral infarction image recognition based on semi-supervised method[J]. Journal of Computer Applications, 2021, 41(4): 1221-1226.

欧莉莉, 邵峰晶, 孙仁诚, 隋毅. 基于半监督方法的脑梗死图像识别[J]. 计算机应用, 2021, 41(4): 1221-1226.

References

[1] 岳丽娟. 医学图像的特征提取与分类方法研究[D]. 无锡:江南大学,2013:7.(YUE L J. Research on medical image feature extraction and classification[D]. Wuxi:Jiangnan University, 2013:7.)
[2] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems. Cambridge:MIT Press,2014:2672-2680.
[3] ODENA A,OLAH C,SHLENS J. Conditional image synthesis with auxiliary classifier GANs[C]//Proceedings of the 34th International Conference on Machine Learning. New York:JMLR. org,2017:2642-2651.
[4] PATHAK D,KRÄHENBÜHL P,DONAHUE J,et al. Context encoders:feature learning by inpainting[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2016:2536-2544.
[5] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook, NY:Curran Associates Inc.,2012:1097-1105.
[6] REED S,AKATA Z,YAN X,et al. Generative adversarial text to image synthesis[C]//Proceedings of the 33rd International Conference on Machine Learning. New York:JMLR. org,2016:1060-1069.
[7] GOODFELLOW I. NIPS 2016 tutorial:generative adversarial networks[EB/OL].[2020-04-15]. https://arxiv.org/pdf/1701.00160.pdf.
[8] ISOLA P,ZHU J Y,ZHOU T,et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE,2017:5967-5976.
[9] 刘坤, 王典, 荣梦学. 基于半监督生成对抗网络X光图像分类算法[J]. 光学学报,2019,39(8):No. 0810003.(LIU K,WANG D,RONG M X. X-ray image classification algorithm based on semisupervised generative adversarial networks[J]. Acta Optica Sinica, 2019,39(8):No. 0810003.)
[10] 唐贤伦, 杜一铭, 刘雨微, 等. 基于条件深度卷积生成对抗网络的图像识别方法[J]. 自动化学报,2018,44(5):855-864. (TANG X L,DU Y M,LIU Y W,et al. Image recognition with conditional deep convolutional generative adversarial networks[J]. Acta Automatica Sinica,2018,44(5):855-864.)
[11] 曾琦, 向德华, 李宁, 等. 基于半监督深度生成对抗网络的图像识别方法[J]. 测控技术,2019,38(8):37-42.(ZENG Q, XIANG D H,LI N,et al. Image recognition based on semi supervised deep convolutional generative adversarial networks[J]. Measurement and Control Technology,2019,38(8):37-42.)
[12] SALIMANS T,GOODFELLOW I,ZAREMBA W,et al. Improved techniques for training GANs[C]//Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook,NY:Curran Associates Inc.,2016:2234-2242.
[13] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL].[2020-04-15]. https://arxiv.org/pdf/1511.06434.pdf.
[14] 谢江荣, 李范鸣, 卫红, 等. 基于生成对抗式神经网络的红外目标仿真方法[J]. 光学学报,2019,39(3):No. 0311002. (XIE J R,LI F M,WEI H,et al. Infrared target simulation method based on generative adversarial neural networks[J]. Acta Optica Sinica,2019,39(3):No. 0311002.)
[15] 张营营. 生成对抗网络模型综述[J]. 电子设计工程,2018,26(5):34-37,43.(ZHANG Y Y. A survey of generative adversarial networks[J]. Electronic Design Engineering,2018,26(5):34-37,43.)
[16] 常亮, 邓小明, 周明全, 等. 图像理解中的卷积神经网络[J]. 自动化学报,2016,42(9):1300-1312.(CHANG L,DENG X M, ZHOU M Q, et al. Convolutional neural networks in image understanding[J]. Acta Automatica Sinica,2016,42(9):1300-1312.)
[17] GOODFELLOW I J,WARDE-FARLEY D,MIRZA M,et al. Maxout networks[C]//Proceedings of the 30th International Conference on International Conference on Machine Learning. New York:JMLR. org,2013:1319-1327.
[18] NAIR V,HINTON G E. Rectified linear units improve restricted Boltzmann machines[C]//Proceedings of the 27th International Conference on International Conference on Machine Learning. New York:JMLR. org,2010:807-814.
[19] XU B,WAN N,CHEN T,et al. Empirical evaluation of rectified activations in convolutional network[EB/OL].[2020-04-15]. http://arxiv.org/pdf/1505.00853.pdf.
[20] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-01-25]. http://arxiv.org/pdf/1409.1556.pdf.
[21] RASMUS A, BERGLUND M, HONKALA M, et al. Semisupervised learning with Ladder networks[EB/OL].[2019-01-25]. https://arxiv.org/pdf/1507.02672.pdf.

Cerebral infarction image recognition based on semi-supervised method

基于半监督方法的脑梗死图像识别

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