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Generative Adversarial Networks for Data Augmentation

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Data Driven Approaches on Medical Imaging

Abstract

One way to expand the available dataset for training AI models in the medical field is through the use of Generative Adversarial Networks (GANs) for data augmentation. GANs work by employing a generator network to create new data samples that are then assessed by a discriminator network to determine their similarity to real samples. The discriminator network is taught to differentiate between actual and synthetic samples, while the generator system is trained to generate data that closely resemble real ones. The process is repeated until the generator network can produce synthetic data that is indistinguishable from genuine data. GANs have been utilized in medical image analysis for various tasks, including data augmentation, image creation, and domain adaptation. They can generate synthetic samples that can be used to increase the available dataset, especially in cases where obtaining large amounts of genuine data is difficult or unethical. However, it is essential to note that the use of GANs in medical imaging is still an active area of research to ensure that the produced images are of high quality and suitable for use in clinical settings.

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Author information

Authors and Affiliations

  1. Research and Development Department, Pioneer Alpha, Dhaka, Bangladesh

    Angona Biswas, Nasim Md Abdullah Al, Al Imran, Anika Tabassum Sejuty & Fabliha Fairooz

  2. School of Computing, Southern Illinois University, Carbondale, IL, USA

    Sai Puppala & Sajedul Talukder

Authors
  1. Angona Biswas
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  2. Nasim Md Abdullah Al
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  3. Al Imran
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  4. Anika Tabassum Sejuty
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  5. Fabliha Fairooz
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  6. Sai Puppala
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  7. Sajedul Talukder
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Corresponding author

Correspondence to Nasim Md Abdullah Al .

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Editors and Affiliations

  1. Norman, OK, USA

    Bin Zheng

  2. Department of Computer Systems Engineering and Technology, Oregon Institute of Technology, Klamath Falls, OR, USA

    Stefan Andrei

  3. Department of Computer Science, Bowie State University, Bowie, MD, USA

    Md Kamruzzaman Sarker

  4. Department of Cyber Physical System, Clark Atlanta University, Atlanta, GA, USA

    Kishor Datta Gupta

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Biswas, A. et al. (2023). Generative Adversarial Networks for Data Augmentation. In: Zheng, B., Andrei, S., Sarker, M.K., Gupta, K.D. (eds) Data Driven Approaches on Medical Imaging. Springer, Cham. https://doi.org/10.1007/978-3-031-47772-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-47772-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47771-3

  • Online ISBN: 978-3-031-47772-0

  • eBook Packages: MedicineMedicine (R0)

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