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Resizing and cleaning of histopathological images using generative adversarial networks

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  • Çelik, Gaffari
  • Talu, Muhammed Fatih

Abstract

Bilinear and Bicubic interpolation techniques are frequently used to increase image resolution. These techniques with data modeling approach are replaced by intelligent systems that can learn automatically from data. SRGAN is a modern Generative Adversarial Network developed as an alternative to classical interpolation techniques. His ability to produce images in super resolution has attracted the attention of many researchers.

Suggested Citation

  • Çelik, Gaffari & Talu, Muhammed Fatih, 2020. "Resizing and cleaning of histopathological images using generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    • Handle: RePEc:eee:phsmap:v:554:y:2020:i:c:s0378437119315146
    DOI: 10.1016/j.physa.2019.122652
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    References listed on IDEAS

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    1. Takahashi, Shuntaro & Chen, Yu & Tanaka-Ishii, Kumiko, 2019. "Modeling financial time-series with generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    2. Ben Ishak, Anis, 2017. "Choosing parameters for Rényi and Tsallis entropies within a two-dimensional multilevel image segmentation framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 521-536.
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