Ever wondered how to use Deep-Learning for Magnetic Resonance Imaging? Matthias Treder will be showing us how!
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.
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.
How do we insure the AI systems we design have clear intent in mind whilst providing consistent value to users and being mindful of ethical considerations? Some of the Systems AI team will cover design thinking and prototyping approaches taken when beginning to design an AI system alongside a range of our favourite examples across industry, research and arts.
Sean Greaves is a Technical Specialist within the IBM UK Systems AI team in London. He focuses on applications of PowerAI and building the IBM AI community. He has gained experience working as a Creative Technologist in Berlin and studied Mechanical Engineering and Design Informatics in Edinburgh. He loves exploring intersections of technology and art and has presented work at Museum of Modern Art NYC and TU Delft.
Machine Learning Engineer, Python fanboy.