Résumé
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.