Abstract
Deep neural networks have been successfully applied to cardiovascular image analysis, with great potential to accelerate clinical workflows and facilitate large-scale studies. In clinical practice, the segmentation of cardiac MR images is often challenging due to scanner or image sequence variations as well as due to pathologies. In many cases, it is not possible to collect sufficient training data that covers all possible sources of variations and pathologies. This talk will review different data augmentation approaches, including adversarial methods, that can be used to develop robust segmentation approaches that show improved model generalisation ability and robustness.
Speaker’s Bio
Daniel Rueckert is Alexander von Humboldt Professor for AI in Medicine and Healthcare at the Technical University of Munich. He is also Professor of Visual Information Processing in the Department of Computing at Imperial College London where he served as Head of the Department of Computing. He has gained a MSc from Technical University Berlin in 1993, a PhD from Imperial College in 1997, followed by a post-doc at King’s College London. In 1999 he joined Imperial College as a Lecturer, becoming Senior Lecturer in 2003 and full Professor in 2005. He has published more than 500 journal and conference articles with over 60,000 citations (h-index 113). He has graduated over 50 PhD students and supervised over 40 post-docs. Professor Rueckert has been awarded an ERC Synergy Grant (2013) and an ERC Advanced Grant (2020). He is an associate editor of IEEE Transactions on Medical Imaging, a member of the editorial board of Medical Image Analysis, Image & Vision Computing, MICCAI/Elsevier Book Series, and a referee for a number of international medical imaging journals and conferences. He has served as a member of organising and programme committees at numerous conferences, e.g. he has been General Co-chair of MMBIA 2006 and FIMH 2013 as well as Programme Co-Chair of MICCAI 2009, ISBI 2012 and WBIR 2012. In 2014, he has been elected as a Fellow of the MICCAI society and in 2015 He was elected as a Fellow of the Royal Academy of Engineering and as fellow of the IEEE. More recently he has been elected as Fellow of the Academy of Medical Sciences (2019) and as fellow of the American Institute for Medical and Biological Engineering (2021).