Virtual Imaging-Guided Harmonization of Chest X-rays Using a Generative Adversarial Neural Network
Key Points
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.
Abstract
The proposed virtual-to-clinical strategy is scalable and generalizable, offering a practical path to standardized CXR appearance and reliable downstream detection across institutions.
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Virtual Imaging-Guided Harmonization of Chest X-rays Using a Generative Adversarial Neural Network | Synapse