Generative Adversarial Networks in Brain Imaging: A Decade-Long Review of Progress and Future Directions
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January 21, 2025
April 11, 2025
Abstract
Due to the increasing demand for effective and objective analysis to address complex challenges such as brain medical image reconstruction, segmentation, and classification, medical image analysis for brain tumor research has gained significant attention. The ability of Generative Adversarial Networks (GANs) to increase the probability density over data distributions by estimating density ratios, along with their capacity to uncover high-dimensional latent distributions, has led to substantial performance improvements in visual feature extraction. Furthermore, the adversarial loss incurred by the discriminator offers a subtle method of incorporating unlabeled samples into training, thereby improving accuracy at higher orders. These characteristics of GANs have proven valuable in various applications, including enhancing medical images and translating images across different modalities. Additionally, the ability of GANs to generate images with remarkable realism offers hope that, through these generative models, the ongoing challenge of limited labelled data in the medical field may be overcome. The aim of this review is to provide a comprehensive overview, starting with a concise summary of the range of available GAN architectures and datasets. This study then highlights the research conducted in processing and interpreting GAN-based brain images. Finally, the limitations of GAN-based methods for brain image analysis are discussed, identifying unresolved research issues and suggesting avenues for further exploration in this emerging field.
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