MirGAN: Medical Image Reconstruction using Generative Adversarial Networks

Computerized tomography scan (or CT scan) is one of the most prominent diagnostic tools used in medical imaging which provides anatomical information about the human body. It uses X-ray machines that rotate and produce 2-dimensional images of the sections of the body under observation and can be visualized easily on the computing devices. There are various deep learning approaches used to reconstruct the medical images by the use of neural networks. In this paper, deep learning based Generative Adversarial Networks (or GAN's) methods have been proposed to reconstruct images from a given randomized vector and trained data. The main goal of the proposed model is to train a better generator than a discriminator in such a way that the reconstructed images are as real as possibly present in the training data.