Deep Learning Applications in Medical Imaging
Imaging the interior of the human body with modern techniques such as computed tomography (CT) or magnetic resonance imaging is a major tool for clinical analysis and treatment of patients. In recent years, methods based on AI and deep learning have led to many signficant advancements in medical imaging applications such as lesion/tumor detection, organ segmentation and others. In this talk, an overview is given of several recent research projects at ZHAW's Centre for AI (CAI) in the domain of deep-learning enabled CT imaging. In particular, in collaboration with the world market leader in the domain of radiation therapy devices, a novel system was developed to reduce artefacts induced by patient motion in reconstructed 3D CT images with the help of deep neural networks. Other applications include the improved diagnosis of COVID-19 based on lung CT scans, as well as the detection and segmentation of the vertebrae forming the spinal column.
Dr. Frank-Peter Schilling is a senior researcher and project manager at the ZHAW Centre for Artificial Intelligence CAI. His research focuses on the application of AI and deep learning in the area of computer vision, as well as on safe, explainable and trustworthy AI. He has a background in particle physics research at CERN and holds a PhD in physics from the University of Heidelberg.
Start date: 25 May 2022, 01.00 pm
ZHAW Digital Health Lab