Radiomics has shown promising results in several medical studies, yet it suers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol and reconstruction parameters. This paper introduces a new method to transform image features in order to improve their stability across scanners. This method is based on a two-layer neural network that can learn a non-linear standardization transformation of various types of features including handcrafted and deep features. In this setting, variations in extracted features will be representative of true physiopathological tissue changes in the scanned patients. This approach uses a publicly available texture phantom dataset and can be applied to both hand-crafted radiomic and deep features.