Uncover This Tech Term: Generative Adversarial Networks
H Shafeeq Ahmed · Korean Journal of Radiology · 2024
iterative process in which the generator and discriminator networks engage to generate increasingly realistic data samples. Examples of Uses of GANs in RadiologyGANs have several applications in radiology.One major challenge in medical imaging is the scarcity of large datasets for training AI algorithms.GANs can be advantageous in overcoming this challenge by generating synthetic medical images, mimicking real patient data with diverse representations of pathologies.This data augmentation significantly expands the training pool, leading to more robust and accurate AI models for disease detection.GANs not only create new data but can also enhance existing images by reducing noise and artifacts, improving visual clarity and diagnostic accuracy.Additionally, GANs can standardize images across different CT protocols and vendors, ensuring uniformity in data representation and improving the reliability of medical imaging data.This is particularly crucial for quantifying regional disease patterns in conditions like interstitial lung disease [5,6].The versatility of GANs extends beyond single modality enhancement.GANs can also seamlessly transform images from one modality to another.For instance, converting chest radiographs to CT-style images facilitates the detection of pulmonary nodules, demonstrating the transformative impact of GANs on cross-modal medical image analysis.Beyond clinical applications, GANs can also be used for medical education [7,8].By generating realistic anatomical structures and pathology scenarios, GANs create AI-driven educational cases and patient models.These virtual simulations serve as valuable tools for training healthcare professionals, allowing them to interact with diverse clinical cases and hone their diagnostic skills.