Prof. Fergus Gleeson

Consultant Radiologist and Professor of Radiology, University of Oxford

Artificial Intelligence: a clinical perspective of the challenges and opportunities [video]

Biography: Professor Fergus Gleeson is a Consultant Radiologist and Professor of Radiology in Oxford. He trained in Cambridge, Papworth and London, and was a Fellow in Radiology at UCLA in America. He was appointed to Oxford in 1992, is Head of Academic Radiology in Oxford, running the ACF and PhD programme, and is the Director of the Oxford Radiology Research Unit at Oxford University Hospitals NHS Foundation Trust. He has published over 200 peer review papers and book chapters, and has more than £20 million in grant income. His specialist interests are in Artificial Intelligence, Thoracic Imaging, PET-CT and Hyperpolarized xenon MRI. Fergus is also the Chief Medical Officer of the National Consortium of Intelligent Medical Imaging (NCIMI): which aims to bring together the NHS, and University and industry partners to promote the development and implementation of artificial intelligence in medicine.

Prof. Michael Bronstein

Chair in Machine Learning and Pattern Recognition, Imperial College London
Head of Graph Learning Research, Twitter

Geometric deep learning on graphs, manifolds, and beyond [video]

Biography: Michael Bronstein is a professor at Imperial College London, where he holds the Chair in Machine Learning and Pattern Recognition, and Head of Graph Learning Research at Twitter. His main research expertise is in theoretical and computational methods for geometric data analysis, a field in which he has published extensively in the leading journals and conferences. He is credited as one of the pioneers of geometric deep learning, generalising machine learning methods to graph-structured data. Michael received his PhD from the Technion (Israel Institute of Technology) in 2007. He has held visiting appointments at Stanford, MIT, Harvard, and Tel Aviv University, and has also been affiliated with three Institutes for Advanced Study (at TU Munich as Rudolf Diesel Fellow (2017-), at Harvard as Radcliffe fellow (2017-2018), and at Princeton (2020)). Michael is the recipient of five ERC grants, Fellow of IEEE and IAPR, 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). He has previously served as Principal Engineer at Intel Perceptual Computing and was a key contributor to the RealSense 3D sensing technology.

Prof. Julia Schnabel

Chair in Computational Imaging, King's College London

Smart Medical Imaging -- from medical image acquisition to analysis and understanding [video]

Abstract: Machine learning, and in particular deep learning approaches in medical imaging have shown great promise in the areas of disease detection, classification and image segmentation, and are now moving into more complex topics such as motion correction and shape modelling. However, their success is limited by the availability and quality of the images in the dataset used for training these algorithms. A common approach is to train deep learning methods on well annotated and curated databases of high-quality image acquisitions, which then may fail on real patient cases in a hospital setting. Another problematic is the lack of sufficient numbers of clinical label annotations in the training data. In this talk I will present some of our recent deep learning approaches that aim to address these two challenges, by applying novel methods for realistic data augmentation and image quality control. To illustrate these approaches, I will draw from our examples in cardiac magnetic resonance imaging, and fetal/maternal ultrasound. I will conclude by giving an outlook on the future opportunities in this field, operating from the imaging sensor right through to extracting clinically relevant measures.

Biography: Julia Schnabel was appointed Chair in Computational Imaging at the School of Biomedical Engineering and Imaging Sciences, King’s College London in 2015. She previously was Professor of Engineering Science (Medical Imaging) at the University of Oxford, in association with a Tutorial Fellowship in Engineering at St. Hilda’s College, Oxford. She holds an MSc in Computer Science (Dipl. Inform.) from the Technical University of Berlin, and a PhD in Computer Science from University College London, University of London. After her PhD she held post-doctoral positions at University College London, King’s College London and University Medical Center Utrecht. Her research is focusing on machine/deep learning, complex motion modelling, as well as multi-modality and quantitative imaging, with applications to cancer, cardiac and fetal imaging. Since 2007 Julia has supervised over 50 PhD students and Postdocs, and is the Director of the EPSRC Centre for Doctoral Training in Smart Medical Imaging, run jointly between King’s College London and Imperial College London. She is an Associate Editor of IEEE Transactions on Medical Imaging, of IEEE Transactions on Biomedical Engineering, on the Editorial Board of Medical Image Analysis, and has recently founded a new free open access journal of Machine Learning for Biomedical Imaging (melba-journal.org) which will be launched in summer 2020. She was a Director of the Medical Imaging Summer School (MISS) on the topics of Computer Vision (2014), Machine Learning (2016) and Deep Learning (2018), Program Chair of the Medical Image Computing and Computer Assisted Intervention (MICCAI) conference (2018), General Chair of the International Workshop in Biomedical Image Registration (WBIR) (2016), and will be a General Chair of the Information Processing in Medical Imaging (IPMI) conference to be held in 2021. She is serving on the IEEE EMBS Administrative Committee and the MICCAI Society Board of Directors. In 2018, Julia was elected Fellow of the MICCAI Society “For contributions to multiple areas of medical image computing, and for distinguished service to the MICCAI conference and Society”, and in 2019 she became Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS) as a member of ELLIS Health.