  While remarkable advances have been made in Computed Tomography (CT),
capturing CT images with non-standardized protocols causes low reproducibility
regarding radiomic features, forming a barrier on CT image analysis in a large
scale. RadiomicGAN is developed to effectively mitigate the discrepancy caused
by using non-standard reconstruction kernels. RadiomicGAN consists of hybrid
neural blocks including both pre-trained and trainable layers adopted to learn
radiomic feature distributions efficiently. A novel training approach, called
Dynamic Window-based Training, has been developed to smoothly transform the
pre-trained model to the medical imaging domain. Model performance evaluated
using 1401 radiomic features show that RadiomicGAN clearly outperforms the
state-of-art image standardization models.
