Machine learning for non-invasive tissue characterization in body imaging

Tissue characterization plays a vital role in the diagnosis of various diseases, as it involves the analysis of the structural, biochemical, and physiological properties of tissues to differentiate healthy tissue from tissue with pathological abnormalities [1]. The current gold standard for tissue characterization is histopathological examination, which involves obtaining a tissue sample through a relatively invasive procedure. Due to this invasiveness, obtaining tissue for histopathological examination can be a painful procedure and carries the risk of hemorrhage.

The aim of this thesis is to determine the role of artificial intelligence (AI) techniques for tissue characterization on medical imaging. More specifically, correlation will be made between the results of medical imaging analysis by AI techniques and the results of histopathological examination, with the ultimate goal to substitute relatively invasive biopsy or surgical procedures needed for histopathological examinations with non-invasive AI-based methods applied on medical imaging.