ELLIS Symposium Workshop on Geometric Deep Learning for Medical Imaging
March 2 @ 2:00 pm - 5:00 pm CET
Title: Equivariance: Trends and Challenges
Abstract: In the natural world symmetries surround us in abundance and so it is of no surprise that we find them present in many datasets. This can be exploited when constructing deep models, such as classifiers or RL agents, by selecting only those operations/layers which preserve chosen symmetries. This design principle, known as equivariance, is the basis for the ubiquity of convolutions in computer vision and other areas. Following developments in the field of equivariance can be difficult due to the accelerated pace in recent years of both ideas and of the mathematical machinery used. In this talk I will collate some of the recent trends in the field and some open challenges that still exist.
Bio: Dr. Daniel Worrall is a machine learning researcher at Qualcomm AI Research, Amsterdam. Previously he was a postdoc in Max Welling’s group with the Amsterdam Machine Learning Lab (AMLAB) at the University of Amsterdam. He did his PhD at University College London in the Machine Vision Group with Gabriel J. Brostow and Dr. Clare Wilson FRCOphth. Before that he studied engineering at The University of Cambridge, and is a scholar of Sidney Sussex College. His current research interests include equivariance and combinatorial optimization, while previously he has worked on uncertainty quantification, unsupervised representation learning, variational inference, normalizing flows, and medical imaging.
Title: Learning on Graphs and Meshes for 3D Blood Vessel Analysis
Abstract: In recent years, deep learning has had a tremendous impact on medical image analysis. Convolutional neural networks (CNNs) allow routine processing of medical images to extract e.g. high-quality triangular mesh segmentations of organs, or graphs representing the structure of vessel trees. A natural next step is to apply machine learning to these representations to extract additional clinically valuable information. However, current CNNs do not generalize to data that is not organized on a regular grid. In this talk, I will review how recent developments in graph and mesh convolutional networks can be leveraged to extract more structural, anatomical, and functional information from medical images, with applications in the improved analysis of 3D blood vessels.
Bio: Jelmer Wolterink is an assistant professor at the Department of Applied Mathematics of the University of Twente. He obtained his PhD in 2017 at Utrecht University (UMC Utrecht) with a thesis entitled Machine learning based analysis of cardiovascular images and was a postdoc in the quantitative image analysis group with Ivana Išgum at UMC Utrecht and Amsterdam UMC, Jelmer’s research interests are in novel machine learning techniques for medical image analysis, such as geometric deep learning and generative modeling. He is involved in several research projects, including an NWO VENI grant that allows him to develop geometric deep learning methods to model the progression of abdominal aortic aneurysms.