16.05 – 16.45 francesco ciompi - Enhancing Digital Pathology with Artificial Intelligence
In this talk, I will present recent results of ongoing projects in computational pathology in my research group. I will show how computational pathology can leverage artificial intelligence to support pathologists in routine diagnostics in a digital pathology workflow. Furthermore, I will show how computational pathology allows to go beyond current practice in routine diagnostics, de facto enhancing digital pathology.
CV Francesco Ciompi is Assistant Professor of Computational Pathology at Radboud University Medical Center, Nijmegen (Netherlands) and Guest Lecturer at Radboud University, Nijmegen (Netherlands). He received the Master's degree in Electronic Engineering from the University of Pisa in July 2006 and the Master's degree in Computer Vision and Artificial Intelligence from the Autonomous University of Barcelona in September 2008. In July 2012 he obtained the PhD (cum laude) in Applied Mathematics and Analysis at the University of Barcelona, with a thesis on "Multi-Class Learning for Vessel Characterization in Intravascular Ultrasound". In February 2013 he joined the Autonomous University of Barcelona as postdoctoral researcher, working on machine learning for computer vision and large scale image classification and retrieval. From September 2007 to September 2013 he was also member of the Computer Vision Center. From 2013 to 2015, he worked as a postdoctoral researcher on automated Chest CT image analysis for efficient lung cancer screening at the Diagnostic Image Analysis Group of Radboud University Medical Center. Since 2015, he is faculty member of the Computational Pathology group of Radboud University Medical Center, working on Deep Learning and Artificial Intelligence for automatic analysis of digital pathology whole-slide images.