  Deep learning algorithms produces state-of-the-art results for different
machine learning and computer vision tasks. To perform well on a given task,
these algorithms require large dataset for training. However, deep learning
algorithms lack generalization and suffer from over-fitting whenever trained on
small dataset, especially when one is dealing with medical images. For
supervised image analysis in medical imaging, having image data along with
their corresponding annotated ground-truths is costly as well as time consuming
since annotations of the data is done by medical experts manually. In this
paper, we propose a new Generative Adversarial Network for Medical Imaging
(MI-GAN). The MI-GAN generates synthetic medical images and their segmented
masks, which can then be used for the application of supervised analysis of
medical images. Particularly, we present MI-GAN for synthesis of retinal
images. The proposed method generates precise segmented images better than the
existing techniques. The proposed model achieves a dice coefficient of 0.837 on
STARE dataset and 0.832 on DRIVE dataset which is state-of-the-art performance
on both the datasets.
