  Deep learning has significant potential for medical imaging. However, since
the incident rate of each disease varies widely, the frequency of classes in a
medical image dataset is imbalanced, leading to poor accuracy for such
infrequent classes. One possible solution is data augmentation of infrequent
classes using synthesized images created by Generative Adversarial Networks
(GANs), but conventional GANs also require certain amount of images to learn.
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.
