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Seminar

Deep Learning in Computational Imaging


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Title

Deep Learning in Computational Imaging


Speaker

Felix Lucka


Abstract

Due to its remarkable success for a variety of complex image processing problems, Deep Learning is nowadays also more commonly used in the domain of computational image reconstruction and inverse problems. In this talk, we will highlight some of the challenges and potential solutions of integrating Deep Learning into computational imaging work-flows found in scientific, clinical or industrial applications using imaging modalities such as X-ray CT, Magnetic Resonance Imaging, Photoacoustic Tomography and Ultrasound.


Bio

After obtaining a first degree in mathematics and physics in 2011, Felix Lucka did a PhD in applied mathematics at WWU Münster (Germany), which included a research visit at UCLA, followed by a postdoc at UCL. Since 2017, he is a tenure track researcher in the Computational Imaging group at the Centrum Wiskunde & Informatica (CWI, Amsterdam). His main interests are mathematical challenges arising from biomedical imaging applications that have a classical inverse problem described by partial differential equations at their core.


When

2021-11-08 at 3:00 pm


Where

Remote