Generative Adversarial Networks for High-Fidelity Medical Image Synthesis and Augmentation
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Generative Adversarial Networks (GANs) have emerged as a transformative technology in the field of artificial intelligence, demonstrating remarkable capabilities in generating highly realistic synthetic data. This chapter explores the application of GANs for high-fidelity medical image synthesis and augmentation, a critical area where data scarcity and privacy concerns often limit the development of robust deep learning models. We provide a comprehensive overview of fundamental GAN concepts and systematically review various architectures, from foundational models like DCGAN to state-of-the-art StyleGANs. A novel GAN-based methodology is proposed, tailored specifically for the challenges of medical imaging, focusing on generating anatomically coherent and diverse images. Through extensive experiments on a publicly available chest X-ray dataset, we demonstrate the superiority of our proposed method over existing techniques. The results are evaluated using a combination of quantitative metrics, including Frechet Inception Distance (FID), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR), as well as through the performance of a downstream segmentation task. Our findings indicate that the synthesized images not only achieve a high degree of realism but also significantly improve the performance of diagnostic models when used for data augmentation. This chapter concludes with a discussion of the clinical implications, ethical considerations, and future research directions for GANs in medical imaging.
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