Other Half-time control Fabian Sinzinger

Principal Supervisor 

Associate Professor Rodrigo Moreno, Division of Biomedical Imaging, Royal Institute of Technology

Co-supervisors 

Associate Professor Joana Pereira, Department of Clinical Neuroscience, Karolinska Institutet 

Professor Örjan Smedby, Division of Biomedical Imaging, Royal Institute of Technology

Half-time review board 

Professor Michael Felsberg, Department of Electrical Engineering, Linköping University

Professor Joakim Lindblad, Department of Information Technology, Uppsala University

Professor Tomas Bjerner, Department of Health, Medicine and Caring Sciences, Linköping University

Brief description

In recent years, general deep learning-based methods have been vastly successful for medical image processing problems. This novel, data-driven methodology introduces high demands on the availability of big datasets for model training, which is an issue in many medical image processing tasks. Geometric deep learning (GDL) is a family of deep learning methods that utilise mathematical concepts of structures, symmetries, and in- or equivariances. Those intrinsic geometric properties of the data and the underlying domains are used here to build problem-specific models. This PhD project demonstrates how GDL can be applied to concrete medical image processing problems. Moreover, the project shows that GDL is a viable alternative for problems with small training datasets. The problems addressed in this thesis include trabecular bone stiffness estimation from micro-CT data, CT-based lung cancer survival rate prediction, tractography extracted from diffusion MRI data, and structural brain connectivity.