Learning from limited and imbalanced medical images with finer synthetic images from gans

HIGHLIGHTS

• who: XIAOLI QIN et al. from the XQin, F, and are with the Department of Electrical and Computer Engineering, University of Z. is with the Department of Electrical and Computer Engineering, University of Houston, Houston, TX , USA have published the research: Learning from Limited and Imbalanced Medical Images with Finer Synthetic Images from GANs, in the Journal: (JOURNAL)
• what: Facing these challenges the authors investigate the effectiveness of using generative models particularly generative adversarial networks (GANs) to synthesize new data to tackle the issue of data paucity and class imbalances. Altogether to verify the robustness . . .
  

   

  

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