AIMI-IBIIS Seminar: Towards Automatic Image-Based Diagnosis and Prognosis - Ivana Isgum, PhD

via Zoom - email aimicenter@stanford.edu for link

Abstract:

Over the past decades, diagnostic imaging has become an integral part of modern medicine. Current increase in the number and volume of the acquired medical images has led to a tremendous increase in the expert workload for image interpretation. In recent years, deep learning has revolutionized many fields including medical imaging. It has shown potential to automate the routine analysis thereby alleviating expert workload, as well as to support clinical research. In this presentation, I will show our recent work on the development of AI-based methods to improve image quality, I will present methods we developed for quantitative analysis of CT and MRI exams, and illustrate their application in large-scale studies.

About:
Ivana Išgum is a Distinguished University Professor in AI and Medical Imaging at the University of Amsterdam. She has appointments at the University Medical Center Amsterdam – location AMC (Departments of Biomedical Engineering and Physics & Radiology and Nuclear Medicine) and Faculty of Science (Informatics Institute), where she leads Quantitative Healthcare Analysis group (qurAI.amsterdam), an interfaculty research group embedded in Faculties of Medicine and Science. Ivana obtained her PhD in 2007 at the Utrecht University. She then worked as a PostDoc in the Leiden University Medical Center and subsequently UMC Utrecht, where she became an Assistant Professor in 2012, and an Associate Professor in 2015. At the Image Sciences Institute of UMC Utrecht, Ivana led the Quantitative Medical Image Analysis (QIA) group, focusing on the development of algorithms for quantitative analysis of medical images to enable automatic patient risk profiling and prognosis using techniques from the fields of machine learning and deep learning. In 2019 Ivana moved with her group to Amsterdam University Medical Center – location AMC, University of Amsterdam. Her current research aims at enhancing patient care by designing and enabling leading-edge AI technologies in healthcare, especially in the fields of radiology, cardiology, and neonatology.