Generative Adversarial Networks in Medical Imaging: A Review of Emerging Trends and Insights

Abstrak

Generative Adversarial Networks (GANs) are increasingly applied in biomedical imaging for realistic image synthesis, enhancement, and modality translation. Despite rapid progress, challenges persist in preserving anatomical accuracy, standardizing evaluation, and achieving reliable clinical integration. This review examines recent GAN-based studies, identifying their architectures, applications, datasets, evaluation methods, strengths, and limitations. The main contribution is a comprehensive synthesis of GAN models across biomedical domains, outlining trends, performance metrics, and research gaps for future exploration. A structured literature review was conducted, analyzing peer-reviewed studies that met predefined inclusion criteria. From the screened records, 66 articles satisfied the eligibility criteria for final inclusion. Data extraction covered GAN types, biomedical applications, dataset characteristics, evaluation metrics, strengths, and limitations. Applications were categorized, and architectures compared to identify thematic and performance patterns. Results indicate research concentration in Medical Physics & Reconstruction and Cancer Imaging & Diagnosis. Advanced models such as StyleGAN, PGGAN, and CycleGAN deliver superior image realism and enhance downstream performance. Frequently used datasets include CBIS-DDSM, ChestX-ray14, BraTS, and OAI. Evaluation relies on SSIM, PSNR, and FID, complemented by clinical metrics such as Dice and AUC. Strengths include improved image quality, diagnostic accuracy gains via augmentation, and adaptability to diverse imaging modalities. Limitations involve dataset dependency, anatomical inaccuracies, training instability, high computational demands, and insufficient clinical validation. In conclusion, GANs show strong potential for biomedical imaging, but advancements are needed to enhance anatomical fidelity, create standardized evaluation protocols, and address ethical considerations to ensure safe clinical deployment.

Kata kunci

• GANs
• Biomedical Imaging
• Image Synthesis
• Deep Learning
• Medical Image Analysis