Deep learning and the Future of Radiology
Biomedical Image Analysis Group
Department of Computing
Imperial College London
The talk will focus on the use of deep learning techniques for the discovery and quantification of clinically useful information from medical images. The talk will describe how deep learning can be used for the reconstruction of medical images from undersampled data, image super-resolution, image segmentation and image classification. It will also show the clinical utility of applications of deep learning for the interpretation of medical images in applications such as brain tumour segmentation, cardiac image analysis and applications in neonatal and fetal imaging. Finally, it will be discussed how deep learning may change the future of medical imaging.
Professor Daniel Rueckert is Head of the Department of Computing at Imperial College London. He founded and leads the Biomedical Image Analysis group consisting of four academics. He has published more than 500 journal and conference articles as well as graduated over 45 PhD students. Professor Rueckert 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 has been elected as Fellow of the Academy of Medical Sciences (2019).
Towards Human-Friendly Explainable Artificial Intelligence
Computational Intelligence Professor
School of Computer Science and Electronic Engineering
University of Essex
The recent advances in computing power coupled with the rapid increases in the quantity of available data has led to a resurgence in the theory and applications of Artificial Intelligence (AI). However, the use of complex AI algorithms could result in a lack of transparency to users which is termed as black/opaque box models. Thus, for AI to be trusted and widely used by governments and industries, there is a need for greater transparency through the creation of human friendly explainable AI (XAI) systems. XAI aims to make machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real-world phenomena. The XAI concept provides an explanation of individual decisions, enables understanding of overall strengths and weaknesses, and conveys an understanding of how the system will behave in the future and how to correct the system’s mistakes. In this keynote speech, Hani Hagras introduce the concepts of XAI by moving towards “explainable AI” (XAI) to achieve a significantly positive impact on communities and industries all over the world and will present novel techniques enabling to deliver human friendly XAI systems which could be easily understood, analysed and augmented by humans. This will allow to the wider deployment of AI systems which are trusted in various real world applications.
Hani Hagras is a Professor of Computational Intelligence, Director of Research and Director of the Computational Intelligence Centre in the University of Essex, UK. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the Institution of Engineering and Technology (IET) and Principal Fellow of the UK Higher Education Academy (PFHEA).
His major research interests are in Explainable Artificial Intelligence, computational intelligence and data science . His research interests also include ambient intelligence, pervasive computing and intelligent buildings. He is also interested in embedded agents, robotics and intelligent control.
He has authored more than 300 papers in international journals, conferences and books. His work has received funding from major research councils and industry. He has also Ten industrial patents in the field of Explainable AI, computational intelligence and intelligent control.
His research has won numerous prestigious international awards where most recently he was awarded by the IEEE Computational Intelligence Society (CIS), the 2013 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems and also he has won the 2004 Outstanding Paper Award in the IEEE Transactions on Fuzzy Systems. He was also awarded the 2015 and 2017 Global Telecommunications Business award for his joint project with British Telecom. In 2016, he was elected as Distinguished Lecturer by the IEEE Computational Intelligence Society. He was also the Chair of the IEEE CIS Chapter that won the 2011 IEEE CIS Outstanding Chapter award. His work with IP4 Ltd has won the 2009 Lord Stafford Award for Achievement in Innovation for East of England. His work has also won the 2011 Best Knowledge Transfer Partnership Project for London and the Eastern Region. His work has also won best paper awards in several conferences including the 2014 and 2006 IEEE International Conference on Fuzzy Systems and the 2012 UK Workshop on Computational Intelligence.
He is an Associate Editor of the IEEE Transactions on Fuzzy Systems. He is also an Associate Editor of the International Journal of Robotics and Automation.
Prof. Hagras chaired several international conferences where he will act as the Programme Chair of the 2021 IEEE International Conference on Fuzzy Systems and he served as the programme chair of the 2017 IEEE International Conference on Fuzzy Systems. He served as the Co-Chair of the 2014, 2013, 2011 and 2009 IEEE Symposium on Intelligent Agents, and the 2011 IEEE International Symposium on Advances to Type-2 Fuzzy Logic Systems. He was also the General Co-Chair of the 2007 IEEE International Conference on Fuzzy systems.
Embedded computer vision and machine learning for drone imaging
Artificial Intelligence Information Analysis Laboratory
Department of Informatics
Aristotle University of Thessaloniki
The aim of drone imaging is: a) to provide information (e.g., semantic 3D maps) for drone mission planning; b) to enhance drone perception for mission execution (e.g., to detect and track targets, avoid obstacle, detect emergency landing sites); c) to provide application dependent visual output. In the last case, it can be used e.g., to visually survey large expanses, ranging, for example, from a stadium to an entire city or employ computational cinematography techniques to create nice footage of sites and events. This keynote lecture will survey innovative intelligent single- and multiple-drone computer vison techniques to address these goals, notably human-centered ones: a) precise semantic 3D mapping using deep semantic image segmentation; b) deep learning for target detection and tracking (e.g., of a human performing a task); c) human activity recognition to detect abnormal events. For safety reasons, most of these tasks should be embedded on drone, using GPU and multicore CPU processing. Fast execution is important, particularly in case where deep video analysis is performed, having large computational load.
This lecture will offer an overview of current research efforts on all related topics, ranging from visual semantic segmentation/mapping to drone perception for autonomous target following, tracking and activity recognition.
Professor Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki, Greece. Since 1994, he has been a Professor at the Department of Informatics of the same University. He served as a Visiting Professor at several Universities.
His current interests are in the areas of image/video processing, machine learning, computer vision, intelligent digital media, human centered interfaces, affective computing, 3D imaging and biomedical imaging. He has published over 1138 papers, contributed in 50 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 9 international journals and General or Technical Chair of 4 international conferences. He participated in 70 R&D projects, primarily funded by the European Union and is/was principal investigator/researcher in 42 such projects. He has 30000+ citations to his work and h-index 81+ (Google Scholar).
Prof. Pitas leads the big European H2020 R&D project MULTIDRONE: https://multidrone.eu/. He is chair of the Autonomous Systems initiative https://ieeeasi.signalprocessingsociety.org/.