GANs Investigation for Multimodal Medical Data Interpretation : Basic Architectures and Overview

Medical imaging technologies have drastically changed the way healthcare professionals diagnose and treat patients. However, medical imaging datasets are limited in terms of size and diversity, which can lead to inaccurate diagnosis and treatment plans. To overcome these limitations, Generative Adversarial Networks (GANs) have been used to generate new medical images based on existing datasets. GANs are a type of deep learning artificial intelligence that can use existing datasets to create new synthetic images that look almost indistinguishable from real images. GANs can be used to generate medical images of organs, tissues, and diseases that are not present in existing datasets, providing healthcare professionals with more accurate and diverse datasets to diagnose and treat patients. GANs for medical image synthesis is a powerful tool that can revolutionize the healthcare industry. In this paper an investigation of the most used Gans architecture are presented. a state of the art of the latest works applying Gans are also discussed based on the adopted modality.