HLSNC-GAN: Medical Image Synthesis Using Hinge Loss and Switchable Normalization in CycleGAN

Abstract

In the field of medical image analysis, MRI and CT, among other multimodal medical images, play crucial roles. To overcome the limitations of image acquisition, researchers have proposed medical image synthesis techniques, including both traditional methods and deep learning approaches. In this study, we introduce a universal framework based on CycleGAN for generating CT images from MRI data.This framework incorporates a Hinge loss function to establish mappings between different modalities and enhance structural consistency between input and output images. We also employ a switchable normalization technique to improve model stability and reduce manual intervention. These enhancements result in the generation of higher-quality synthetic images while avoiding gradient issues and mode collapse.The results of this research demonstrate significant progress in medical image synthesis. Compared to existing methods, our model exhibits superior performance in quantitative evaluation metrics while maintaining better diversity and structural consistency. This indicates that our framework holds promise in medical image synthesis and can provide valuable support in areas such as disease prediction and treatment.