Giuseppe Mastrorocco

Over the last decade, artificial intelligence (AI) and deep learning have transformed many scientific fields, including medical imaging. Despite significant progress, their full potential has yet to be realized in both clinical and preclinical settings. Preclinical imaging, which plays a crucial role in studying diseases and testing new therapies before human application, often faces challenges related to image quality and radiation exposure.

This research aims to develop and evaluate AI-based methods to enhance the quality of preclinical images acquired with various imaging modalities. The main objective is to reduce image noise and artifacts that arise when limited radiation doses are used, allowing for safer and faster imaging without compromising diagnostic quality.

Ultimately, this work supports safer and more optimized imaging protocols, contributing to better disease understanding and potentially improving cancer diagnosis and monitoring in both preclinical and clinical settings.
 

Giuseppe T. Mastrorocco completed his bachelor’s degree in Medical Systems Engineering at Politecnico di Bari (Italy) in 2022. He then earned his master’s degree in Biomedical Engineering at Politecnico di Torino (Italy) in 2025, after participating in the Erasmus+ mobility program at Ghent University. During his stay in Belgium, he carried out his master’s thesis, focusing on introducing interactivity into automatic AI-based methods for the segmentation of clinical CT images. In 2025, he also joined the MEDISIP research group at Ghent University to pursue a PhD in Biomedical Engineering, focusing on artificial intelligence for medical imaging.
 

• Lab address: 
  IBiTech-MEDISIP, Campus UZ Gent, C. Heymanslaan 10, 
  The Core, entrance 37a, 9000 Gent, Belgium
• MEDISIP research group
• LinkedIn
• ORCID  
• Giuseppe Mastrorocco is interested to receive invitations for presentations or talks