Enhancing radiomics and Deep Learning systems through the standardization of medical imaging workflows

Abstract

Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical workflows is a necessary step towards generalization and interoperability of radiomics and artificial intelligence algorithms in medical imaging.