Deep Learning for Magnetic Resonance Imaging
This talk focuses on deep learning applied to 3D structural Magnetic Resonance Images (MRIs) of the human brain. It starts with a broad overview of deep learning for medical imaging including the challenges faced when working with 3D images. Subsequently, two ongoing research projects will be introduced. In the first (more theoretical) project, a novel weight-sharing technique is discussed that builds on reflection symmetry. The technique has been implemented in Tensorflow as a novel convolutional layer type. In the second (more applied) project, I will present our "noise-to-brain" model, a Generative Adversarial Network (GAN) that generates MRIs of the brain.
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Deep Learning for Magnetic Resonance Imaging
Matthias Treder
After completing a PhD in visual perception at Radboud University Nijmegen, the Netherlands, Matthias Treder worked at the Machine Learning Lab of Prof. Klaus-Robert Mueller in Berlin from 2009-2014. Subsequently, he moved to the United Kingdom, working on the application of machine learning to neuroimaging data at the universities of Cambridge, Birmingham, and Cardiff. In 2018, he was appointed as Lecturer in Data Science & Analytics at Cardiff University.