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Biograpgy

Assistant Professor - Istanbul Technical University

Dr. Ilkay Oksuz is currently an Assistant Prof. in Computer Engineering Department of Istanbul Technical University. His current research interests are in machine learning and deep learning, with a focus on medical image quality assessment and medical image reconstruction . He is currently leading the Predictive Intelligence and Medical Imaging (PIMI) Lab. He studied for a PhD at the IMT Institute for Advanced Studies Lucca on Computer, Decision, and Systems Science under the supervision of Prof Sotirios Tsaftaris. His PhD thesis focused on joint registration and segmentation of the myocardium region in MR sequences. He joined the Diagnostic Radiology Group at Yale University in 2015 for 10 months as a Postgraduate Fellow, where he worked under the mentorship of Prof Xenios Papademetris. He also worked at the University of Edinburgh Institute for Digital Communications department for six months in 2017.

presentation

Automatic Quality Assessment of Cardiac MRI using Deep Learning Techniques

Cardiovascular disease is the major cause of mortality in the world. Recently, Cardiovascular Magnetic Resonance techniques have gained ground in diagnosis of cardiovascular disease and good quality of such MR images is a prerequisite for the success of subsequent image analysis pipelines. Quality assessment of medical images is therefore an essential activity and for large population studies such as the UK Biobank, manual identification of artefacts such as those caused by unanticipated motion is tedious and time-consuming. In this talk, recent work on detection of wrong cardiac planning and cardiac motion artefacts using deep learning techniques will be described. The details of deep learning architectures and machine learning methodologies will be given with a certain focus on synthetic k-space corruption and curriculum learning techniques. In the last part of the talk, the mechanisms to correct image artefacts will be discussed with their influence on segmentation.