2nd Workshop on GRaphs in biomedicAl Image anaLysis

Prof. Michael Bronstein
Imperial College / USI Lugano

Deep Learning on Graphs

Bio : Michael Bronstein (PhD with distinction 2007, Technion, Israel) is a professor at USI Lugano, Switzerland and Imperial College London, UK where he holds the Chair in Machine Learning and Pattern Recognition and Royal Society Wolfson Merit Award. During 2017-2018 he was a fellow at the Radcliffe Institute for Advanced Study at Harvard University. Michael's main research interest is in theoretical and computational methods for geometric data analysis. He authored over 150 papers, the book Numerical geometry of non-rigid shapes (Springer 2008), and over 20 granted patents. He was awarded four ERC grants, two Google Faculty Research awards, Amazon AWS Machine Learning award, and Rudolf Diesel fellowship (2017) at TU Munich. He was invited as a Young Scientist to the World Economic Forum, an honor bestowed on forty world’s leading scientists under the age of forty. Michael is a Senior Member of the IEEE, alumnus of the Technion Excellence Program and the Academy of Achievement, ACM Distinguished Speaker, and a member of the Young Academy of Europe. In addition to academic work, Michael is actively involved in commercial technology development and consulting to start-up companies. He was a co-founder and technology executive at Novafora (2005-2009) developing large-scale video analysis methods, and one of the chief technologists at Invision (2009-2012) developing low-cost 3D sensors. Following the multi-million acquisition of Invision by Intel in 2012, Michael has been one of the key developers of the Intel RealSense technology in the role of Principal Engineer.

Prof. Dimitri Van De Ville
EPFL / University of Geneva

Graph Signal Processing Opens New Perspectives for Human Brain Imaging

Abstract: State-of-the-art magnetic resonance imaging (MRI) provides unprecedented opportunities to study brain structure (anatomy) and function (physiology). Based on such data, graph representations can be built where nodes are associated to brain regions and edge weights to strengths of structural or functional connections. In particular, structural graphs capture major neural pathways in white matter, while functional graphs map out statistical interdependencies between pairs of regional activity traces. Network analysis of these graphs has revealed emergent system-level properties of brain structure or function, such as efficiency of communication and modular organization. In this talk, graph signal processing (GSP) will be presented as a novel framework to integrate brain structure, contained in the structural graph, with brain function, characterized by activity traces that can be considered as time-dependent graph signals. Such a perspective allows to define novel meaningful graph-filtering operations of brain activity that take into account the anatomical backbone. For instance, we will show how activity can be analyzed in terms of being aligned versus liberal with respect to brain structure, or how additional prior information about cognitive systems can be incorporated. The well-known Fourier phase randomization method to generate surrogate data can also be adapted to this new setting. Finally, recent work will highlight how the spatial resolution of this type of analyses can be increased to the voxel level, representing a few ten thousands of nodes.

Bio: Dimitri Van De Ville (Senior Member, IEEE) received the M.S. degree in engineering and computer sciences and the Ph.D. degree from Ghent University, Ghent, Belgium, in 1998, and 2002, respectively. After a postdoctoral stay (2002-2005) at the lab of Prof. M. Unser at the Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, he became responsible for the Signal Processing Unit at the University Hospital of Geneva, Geneva, Switzerland, as part of the Centre d'Imagerie Biomédicale (CIBM). In 2009, he received a Swiss National Science Foundation professorship and since 2015 became Professor of Bioengineering at the EPFL and the University of Geneva, Geneva, Switzerland. His research interests include wavelets, sparsity, graphs, pattern recognition, and their applications in computational neuroimaging.

Dr. Van De Ville served as an Associate Editor for the IEEE Transactions on Image Processing from 2006 to 2009 and the IEEE Signal Processing Letters from 2004 to 2006, as well as Guest Editor for several special issues. He was the Chair of the Bio Imaging and Signal Processing (BISP) Technical Committee of the IEEE Signal Processing Society (2012-2013) and is the Founding Chair of the EURASIP Biomedical Image & Signal Analytics SAT. He is Co-Chair of the biennial Wavelets & Sparsity series conferences, together with V. Goyal, Y. Lu, and M. Papadakis. He was a recipient of the Pfizer Research Award 2012, the NARSAD Independent Investigator Award 2014, and the Leenaards Foundation Award 2016.