Journal of Machine Learning and Applications

ABSTRACT

Generative Adversarial Networks (GANs) have emerged as transformative tools in medical imaging, addressing critical challenges such as limited datasets, low image quality, and the need for enhanced diagnostic precision. By leveraging adversarial training, GANs generate highly realistic synthetic images, which are invaluable for data augmentation and improving the robustness of machine learning models. Applications of GANs span diverse areas, including super-resolution imaging to enhance low-quality scans, image-to-image translation for cross-modality data synthesis, and artifact removal to improve diagnostic reliability. GANs also play a pivotal role in simulating pathological scenarios, providing synthetic datasets for training and testing diagnostic algorithms, especially for rare or ethically sensitive conditions. Despite their potential, GANs face technical challenges such as dataset bias, computational demands, and instability during training, which can limit the generalizability of their outputs. Ethical concerns, including the misuse of synthetic data, the indistinguishability between real and synthetic images, and privacy risks, further complicate their application. Regulatory hurdles also pose barriers to clinical adoption, necessitating robust frameworks for validation and integration. Future research directions include the integration of multi-modal data, federated learning for privacy-preserving collaborations, innovations in explainable AI, and real-time applications in telemedicine. Addressing these challenges will be crucial to realizing the full potential of GANs in revolutionizing medical imaging and healthcare.

KEYWORDS

1. Generative adversarial networks
2. Medical imaging
3. Synthetic data augmentation
4. Super-resolution imaging
5. Federated learning