GeoMedIA Workshop 2022

In this workshop, we aim to draw attention to current developments in geometric deep learning for medical image analysis. The main objective of our workshop is to expose the vast richness of geometric structure to be found in medical image data and show how to leverage it in neural network design. We will provide a discussion on the latest state-of-the-art by having invited expert speakers on the topic. We also aim to cover current challenges and opportunities in the area. Our objective is to inspire researchers through a day of exciting keynotes and contributed talks, showing how to design and/or apply methods that leverage geometric structure in imaging problems, e.g., through group convolutions, mesh CNNs, or graph neural networks with geometric priors. The objectives of the Geometric deep learning in medical image analysis (GeoMedIA) workshop are to (a) bring together experts on geometric deep learning in medical image analysis to push the state of the art; (b) hear from invited speakers, and (c) to identify challenges and opportunities for further research.

Michael Bronstein is the DeepMind Professor of AI at the University of Oxford and Head. He was previously a professor at Imperial College London and held visiting appointments at Stanford, MIT, and Harvard, and has also been affiliated with three Institutes for Advanced Study (at TUM as a Rudolf Diesel Fellow (2017-2019), at Harvard as a Radcliffe fellow (2017-2018), and at Princeton as a short-time scholar (2020)). Michael received his PhD from the Technion in 2007. He is the recipient of the Royal Society Wolfson Research Merit Award, Royal Academy of Engineering Silver Medal, five ERC grants, two Google Faculty Research Awards, and two Amazon AWS ML Research Awards. He is a Member of the Academia Europaea, Fellow of IEEE, IAPR, BCS, and ELLIS, ACM Distinguished Speaker, and World Economic Forum Young Scientist. In addition to his academic career, Michael is a serial entrepreneur and founder of multiple startup companies, including Novafora, Invision (acquired by Intel in 2012), Videocites, and Fabula AI (acquired by Twitter in 2019).

Emma Robinson's research focuses on the development of computational methods for brain imaging analysis, and covers a wide range of image processing and machine learning topics. Most notably, her software for cortical surface registration (Multimodal Surface Matching, MSM) has been central to the development of of the Human Connectome Project’s “Multi-modal parcellation of the Human Cortex “ (Glasser et al, Nature 2016), and has featured as a central tenet in the HCP’s paradigm for neuroimage analysis (Glasser et al, Nature NeuroScience 2016). This work has been widely reported in the media including Wired, Scientific American, and Wall Street Journal). Current research interests are focused on the application of advanced machine learning, and particularly Deep Learning to diverse data sets combining multi-modality imaging data with genetic samples. We are particualrly interested in building sensitive models of cognitive development and developmental outcome for prematurely born babies from data collected for the Developing Human Connectome Project (dHCP).

GeoMedIA will be held in the CASA hotel in Amsterdam as a one-day workshop. Registration for GeoMedIA is free thanks to our generous sponsors. To register for GeoMedIA, please use the form below. Note that we have a limited number of places available, and may be unable to accommodate all applicants. Registration will be confirmed on a first-come first-serve basis. We also provide a limited number of travel grants for those requiring support for their travel to and from Amsterdam. If you want to apply for one of these grants, please provide the required information in the registration form. Update November 10: GeoMedIA is at capacity! The registration form has now been closed. Use the form below to be added to our waiting list.