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Cancer classification with data augmentation based on generative adversarial networks

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Abstract

Accurate diagnosis is a significant step in cancer treatment. Machine learning can support doctors in prognosis decision-making, and its performance is always weakened by the high dimension and small quantity of genetic data. Fortunately, deep learning can effectively process the high dimensional data with growing. However, the problem of inadequate data remains unsolved and has lowered the performance of deep learning. To end it, we propose a generative adversarial model that uses non target cancer data to help target generator training. We use the reconstruction loss to further stabilize model training and improve the quality of generated samples. We also present a cancer classification model to optimize classification performance. Experimental results prove that mean absolute error of cancer gene made by our model is 19.3% lower than DC-GAN, and the classification accuracy rate of our produced data is higher than the data created by GAN. As for the classification model, the classification accuracy of our model reaches 92.6%, which is 7.6% higher than the model without any generated data.

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Acknowledgements

This work was supported by National Key R&D Program of China (2018YFB1402600, 2017YFB0802203), the National Natural Science Foundation of China (Grant Nos. 61972178, 61702043, 61906075, 61932010), Key-Area Research and Development Program of Guangdong Province (2019B010137005), Natural Science Foundation of Guangdong Province (2017A030313334, 2019A1515011753, 2019A151 5011920), Science and Technology Program of Guangzhou of China (201802010061) and Beijing Natural Science Foundation (4194086).

Author information

Authors and Affiliations

  1. College of Information Science and Technology, Jinan University, Guangzhou, 510632, China

    Kaimin Wei, Tianqi Li, Feiran Huang & Zefan He

  2. Guangdong Key Laboratory of Data Security and Privacy Protection, Guangzhou, 510632, China

    Kaimin Wei, Tianqi Li, Feiran Huang & Zefan He

  3. School of Software Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Jinpeng Chen

Authors
  1. Kaimin Wei
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  2. Tianqi Li
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  3. Feiran Huang
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  4. Jinpeng Chen
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  5. Zefan He
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Corresponding author

Correspondence to Feiran Huang.

Additional information

Kaimin Wei is now an associate professor at the College of Information Science and Technology in Jinan University, China. He received the PhD degree from Beihang University, China. His research interests include mobile networks, crowd sensing, mechine learning and AI security.

Tianqi Li is a graduate student at the College of Information Science and Technology in Jinan University, China. She received the bechelor degree in computer science and technology from Guizhou University, China. Her research interests include bioinformatics, computer vision, and machine learning.

Feiran Huang received his BSc degree from Central South University, China in 2011. He received his PhD degree in computer software and theory from School of Computer Science and Engineering, Beihang University, China in 2019. He is currently a lecturer at College of Information Science and Technology & College of Cyber Security, Jinan University, China. He has published over 10 papers, such as TIP, TOMM, TCYB, TITS, ACM MM, CIKM, and ICMR. His research interests include social media analysis and multi-modal learning.

Jinpeng Chen is now an associate professor at the School of Software Engineering, Beijing University of Posts and Telecommunications, China. His research interests include social network analysis, recommendation system, data mining, and machine learning.

Zefan He is an undergraduate majoring in Computer Science and Technology in Jinan University, China. Her research interests include crowdsensing, blockchain, and machine learning.

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Wei, K., Li, T., Huang, F. et al. Cancer classification with data augmentation based on generative adversarial networks. Front. Comput. Sci. 16, 162601 (2022). https://doi.org/10.1007/s11704-020-0025-x

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  • DOI: https://doi.org/10.1007/s11704-020-0025-x

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