  One of the most challenging aspects of medical image analysis is the lack of
a high quantity of annotated data. This makes it difficult for deep learning
algorithms to perform well due to a lack of variations in the input space.
While generative adversarial networks have shown promise in the field of
synthetic data generation, but without a carefully designed prior the
generation procedure can not be performed well. In the proposed approach we
have demonstrated the use of automatically generated segmentation masks as
learnable class-specific priors to guide a conditional GAN for the generation
of patho-realistic samples for cytology image. We have observed that
augmentation of data using the proposed pipeline called "SynCGAN" improves the
performance of state of the art classifiers such as ResNet-152, DenseNet-161,
Inception-V3 significantly.
