• Corpus ID: 54458438

General-to-Detailed GAN for Infrequent Class Medical Images

@article{Koga2018GeneraltoDetailedGF,
  title={General-to-Detailed GAN for Infrequent Class Medical Images},
  author={Tatsuki Koga and Naoki Nonaka and Jun Sakuma and Jun Seita},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.01690}
}
Deep learning has significant potential for medical imaging. [] Key Method To overcome this limitation, here we propose General-to-detailed GAN (GDGAN), serially connected two GANs, one for general labels and the other for detailed labels. GDGAN produced diverse medical images, and the network trained with an augmented dataset outperformed other networks using existing methods with respect to Area-Under-Curve (AUC) of Receiver Operating Characteristic (ROC) curve.
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