Virtual Imaging-Guided Harmonization of Chest X-rays Using a Generative Adversarial Neural Network

Puntos clave

• Reliable detection of chest X-rays is achieved through a generative adversarial neural network approach, enhancing standardization.
• The proposed virtual-to-clinical strategy ensures scalable, generalizable results across different healthcare settings.
• This analysis leverages advanced imaging-guided harmonization methods to improve CXR appearances uniformly.
• The findings suggest broad clinical implications for AI-driven imaging approaches, requiring external validation.