  Automation in medical imaging is quite challenging due to the unavailability
of annotated datasets and the scarcity of domain experts. In recent years, deep
learning techniques have solved some complex medical imaging tasks like disease
classification, important object localization, segmentation, etc. However, most
of the task requires a large amount of annotated data for their successful
implementation. To mitigate the shortage of data, different generative models
are proposed for data augmentation purposes which can boost the classification
performances. For this, different synthetic medical image data generation
models are developed to increase the dataset. Unpaired image-to-image
translation models here shift the source domain to the target domain. In the
breast malignancy identification domain, FNAC is one of the low-cost
low-invasive modalities normally used by medical practitioners. But
availability of public datasets in this domain is very poor. Whereas, for
automation of cytology images, we need a large amount of annotated data.
Therefore synthetic cytology images are generated by translating breast
histopathology samples which are publicly available. In this study, we have
explored traditional image-to-image transfer models like CycleGAN, and Neural
Style Transfer. Further, it is observed that the generated cytology images are
quite similar to real breast cytology samples by measuring FID and KID scores.
