Samuel Armato

Samuel Armato is a Professor in the Department of Radiology-Basic Sciences at the University of Chicago. His research focuses on medical image analysis, computer-aided diagnosis, and artificial intelligence in medical imaging, particularly in computed tomography (CT) scans for lung cancer and mesothelioma. Armato has extensive experience in multi-institutional imaging projects, serving as the local principal investigator for the Lung Image Database Consortium and as Faculty Director of the University of Chicago’s Human Imaging Research Office.

• Education: Ph.D. in Medical Physics (1997), University of Chicago; B.A. in Physics (1987), University of Chicago

His work spans radiomics, image quality assessment, and tumor response evaluation, with a strong emphasis on CT imaging for thoracic diseases. Recent publications highlight applications of deep learning to COVID-19 classification and radiomics-based differentiation of BAP1 mutations in mesothelioma. Armato also contributes to standardization of imaging criteria and quality assurance in clinical trials.

Scientific Awards:

• iMig Advancement Award (2021)
• SPIE Fellow (2018)
• Academy of Radiology Research Distinguished Investigator (2016)
• AAPM Fellow (2014)
• Kurt Rossmann Teaching Award (2012, 2002)
• Raine Visiting Professor (2009)

Armato has led NIH-funded projects such as Acuo PACS system, Computerized Analysis of Mesothelioma, and Standard Database for CT Lung Images. He collaborates on multidisciplinary teams and contributes to clinical trial imaging protocols and radiation therapy optimization.