ConText-GAN: using contextual texture information for realistic and controllable medical image synthesis

Marc-Adrien Hostin, Shahram Attarian, David Bendahan, Marc-Emmanuel Bellemare

Abstract: This study proposes an enhancement to the ConText-GAN, an image synthesis model using a controllable texture input. The improvement consists in using a texture feature fusion module to reduce the complexity of the model, and enable the use of the OASIS architecture for image generation.