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Brain Functional Connectivity Augmentation Method for Mental Disease Classification with Generative Adversarial Network

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Book cover Pattern Recognition and Computer Vision (PRCV 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11857))

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Abstract

Functional magnetic resonance imaging (fMRI) plays a significant role in the study and analysis of brain cognitive function. In the existing fMRI classification research, because of the small number of trainable samples, it is easily over-fitted in the classification task. In this paper, we propose an improved deep learning generative adversarial network (GAN) to augment fMRI functional connectivity data. The network has the data augmentation ability using Wasserstein distance and double-class distance constraint to augment the data of subjects and control groups. Finally, the data generated by the GAN are used to improve the ability of the classifier. We investigated two brain disorders, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), and evaluated the classification performance of the model in these two diseases. The results revealed that compared to existing classifiers, classification accuracy was greatly improved after data augmentation by the GAN. In addition, compared to several common deep network data generation methods, the performance of the proposed network is significantly better.

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Acknowledgments

This study was supported by the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Project No. SJCX18_0741). Scientific Research Foundation for Advanced Talents of Jiangsu University (Project No. 14JDG040).

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212200, China

    Qi Yao & Hu Lu

Authors
  1. Qi Yao
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  2. Hu Lu
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Corresponding author

Correspondence to Hu Lu .

Editor information

Editors and Affiliations

  1. School of EECS, Peking University, Beijing, China

    Zhouchen Lin

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Liang Wang

  3. Nanjing University, Nanjing, Jiangsu, China

    Jian Yang

  4. Xidian University, Xi'an, China

    Guangming Shi

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Institute of Artificial Intelligence, Xi'an Jiaotong University, Xi'an, Shaanxi, China

    Nanning Zheng

  7. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  8. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

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Yao, Q., Lu, H. (2019). Brain Functional Connectivity Augmentation Method for Mental Disease Classification with Generative Adversarial Network. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_38

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  • DOI: https://doi.org/10.1007/978-3-030-31654-9_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31653-2

  • Online ISBN: 978-3-030-31654-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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