Professor Frangi is Diamond Jubilee Chair in Computational Medicine at the University of Leeds, Leeds, UK, with joint appointments at the School of Computing and the School of Medicine. He leads the CISTIB Center for Computational Imaging and Simulation Technologies in Biomedicine. He has been awarded a Royal Academy of Engineering Chair in Emerging Technologies (2019-2029).
Professor Frangi has edited several books, published 7 editorial articles and over 215 journal papers in key international journals of his research field and more than over 200 book chapters and international conference papers with an h-index 55 and over 20,700 citations according to Google Scholar. He has been three times Guest Editor of special issues of IEEE Trans Med Imaging, one on IEEE Trans Biomed Eng, and one of Medical Image Analysis journal. He was chair of the 3rd International Conference on Functional Imaging and Modelling of the Heart (FIMH05) held in Barcelona in June 2005, Publications Chair of the IEEE International Symposium in Biomedical Imaging (ISBI 2006), Programme Committee Member of various editions of the Intl Conf on Medical Image Computing and Computer Assisted Interventions (MICCAI) (Brisbane, AU, 2007; Beijing CN, 2010; Toronto CA 2011; Nice FR 2012; Nagoya JP 2013), International Liaison of ISBI 2009, Tutorials Co-Chair of MICCAI 2010, and Program Co-chair of MICCAI 2015. He was also General Chair for ISBI 2012 held in Barcelona. He is the General Chair of MICCAI 2018 held in Granada, Spain.
Erik Meijering is a Professor of Biomedical Image Computing with a joint appointment in the School of Computer Science and Engineering and the Graduate School of Biomedical Engineering. His research focuses on the development of innovative computer vision methods for automated quantitative analysis of biomedical imaging data.
He received the MSc degree in Electrical Engineering from Delft University of Technology (Delft, the Netherlands) in 1996 and the PhD degree in Medical Image Analysis from Utrecht University (Utrecht, the Netherlands) in 2000. Before coming to the University of New South Wales (UNSW, Sydney, Australia) in 2019, he was a Postdoctoral Fellow at the Swiss Federal Institute of Technology (Lausanne, Switzerland) from 2000-2002, an Assistant Professor at Erasmus University Medical Center (Rotterdam, the Netherlands) from 2002-2008, and an Associate Professor at the same institute from 2008-2019.
Since 2019 he is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). Within IEEE he is a member of the Signal Processing Society (SPS) and the Engineering in Medicine and Biology Society (EMBS), and served/serves on the SPS Technical Committee on Bio Imaging and Signal Processing (BISP) (2005-2010 and 2017-2020), the SPS Technical Directions Board (2018-2019), the EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP) (since 2007), and the cross-Society IEEE Life Sciences Technical Community (LSTC) (since 2018).
Over the years he was/is an Associate Editor for the IEEE Transactions on Medical Imaging (since 2004), the International Journal on Biomedical Imaging (2006-2009), the IEEE Transactions on Image Processing (2008-2011), has co-edited various journal special issues on topics related to biomedical imaging, and has co-organized various conferences in the field, notably the IEEE International Symposium on Biomedical Imaging (ISBI 2002, 2004, 2006, 2010, 2015, 2018). He also served/serves on a great variety of other international conference, advisory, and review boards.
Raphael Sznitman received his B.Sc. in cognitive science from the University of British Columbia (Canada) in 2007. Following this, he studied computer science at Johns Hopkins University (USA) where he received his M.Sc and PhD. From 2011 to 2014, he was a postdoctoral fellow at the École Polytechnique Fédérale de Lausanne (Switzerland) in the Computer Vision Laboratory. Now an Assistant Professor at the ARTORG Center for Biomedical Engineering Research of the University of Bern (Switzerland), his research interests lie in the fields of computer vision and machine learning with applications to biomedical imaging, surgery and histology.
Dr. Strand is the professor in Computerized Image Analysis, head of the Division of Visual Information and Interaction, Dept. of Information Technology, Uppsala University. He obtained his PhD and Master degree from Uppsala University. His research interests are on image analysis, medical image processing, and digital geometry.
Speech Title: Large cohort analysis in medical image processing
The massive amount of medical image data being made available in both research and clinical work today is often too big to be parsed by human experts. Computer-aided tools have a great potential for detecting patterns in the medical image data, and to find relationships between image data and other medical data. The computer assisted methods often performs better than human experts, resulting in improved disease understanding.
This talk will focus on two specific methods for large scale medical image data analysis developed and used in our group – (i) Imiomics, which enables statistical analyses of relations between whole body image data in large cohorts and other non-imaging data, at an unprecedented level of detail/spatial resolution, and (ii) aggregated saliency analysis, which describe which image regions on average had the highest impact on network predictions in regression analysis in large cohorts.
Kensaku Mori received the B.Eng. degree in electronics engineering, and the M.Eng. and Ph.D. degrees in information engineering from Nagoya University, Nagoya, Japan, in1992, 1994, and 1996, respectively. He is a Professor with the Graduate School of Informatics, Nagoya University, and the Director of Information Technology Center of Nagoya University and an MICCAI Fellow.,Dr. Mori is currently involved in many international conference organizations, including SPIE Medical Imaging, CARS, IPCAI and MICCAI, as a General Chair or program committee members. He is a Member of IEEE, SPIE, ISCAS, IEICE, JSCAS, JSMBE, and JAMIT. He was the recipient of many awards including Young Scientist Award from the Minister of Education, Culture, Sports, Science and Technology, and RSNA Magna Cum Laude. (Based on document published on 9 July 2018).
Prof. Yu-Dong Zhang received his PhD degree from Southeast University in 2010. He worked as a postdoc from 2010 to 2012 in Columbia University, USA, and as an assistant research scientist from 2012 to 2013 at Research Foundation of Mental Hygiene (RFMH), USA. He served as a full professor from 2013 to 2017 in Nanjing Normal University, where he was the director and founder of Advanced Medical Image Processing Group in NJNU. Now he serves as Professor in Department of Informatics, University of Leicester, UK.
He was included in “Most Cited Chinese researchers (Computer Science)” by Elsevier from 2014 to 2018. He was the 2019 recipient of “Highly Cited Researcher” by Web of Science. He won “Emerald Citation of Excellence 2017” and “MDPI Top 10 Most Cited Papers 2015”. He was included in "Top Scientist" in Guide2Research. He published over 160 papers, including 16 “ESI Highly Cited Papers”, and 2 “ESI Hot Papers”. His citation reached 10096 in Google Scholar, and 5362 in Web of Science.
He is the fellow of IET (FIET), and the senior members of IEEE and ACM. He is the editor of Scientific Reports, IEEE Transactions on Circuits and Systems for Video Technology, etc. He served as the (leading) guest editor of Information Fusion, Neural Networks, IEEE Transactions on Intelligent Transportation Systems, etc. He has conducted many successful industrial projects and academic grants from NSFC, NIH, Royal Society, and British Council.
Speech Title: Medical Image Analysis via Deep Learning
Professor Soleimani leads the Engineering Tomography Lab (ETL) group he found in 2011. ETL works in partnership with leading UK and international research teams and many industrial partners.
Particular interests are in ultrasound tomography, electrical and electromagnetic imaging and X-ray CT, nonlinear inverse problems, machine learning, and multi-modality imaging.
The main areas are in, "Machine Learning", "AI", “Super-Sensing”, "X-ray CT", "Ultrasound tomography", “Multi-modality Tomography”, “Inverse Problems”, and “Electrical and Electromagnetic Tomography”, "Bio-impedance imaging", including: 1) Medical imaging , 2) Industrial process tomography, 3) Tomography for material characterisation.
Professor Soleimani has led the University of Bath's collaborations with CERN through CMS research and educational partnership established from 2019. This followed a highly successful medical imaging project between Bath and CERN leading to a joint open source software platform TIGRE.
Speech Title: Multi modality tomographic imaging for structural and functional imaging
Dr Edwin Abdurakman is the programme director for Diagnostic Radiography undergraduate studies in the School of Health Sciences at City, University of London. He acquired his PhD from the Nottingham Trent University in the medical applications of Magnetic Resonance Imaging (MRI). His novel research focused on developing and testing microbubble-based contrast agents to measure pressure variations in the human stomach with MRI, a collaborative work between the Nottingham Trent University and the world distinguished MRI research centre, the Sir Peter Mansfield Imaging Centre at the University of Nottingham. The research aimed to use microbubbles as a pressure probe for MRI gastrointestinal applications, which will be useful as a diagnostic tool in diagnosing patients with functional dyspepsia conditions. His latest article has been published in the highly rated peer-reviewed journal Magnetic Resonance in Medicine.
Dr Abdurakman holds a Fellowship of the Higher Education Academy. He is an appointed member of the British Institute of Radiology (BIR) MR special interest group, and an active member of the Society and College of Radiographers (SCoR) as well as the European Society of Magnetic Resonance in Medicine and Biology (ESMRMB).
Speech Title: Measuring pressure in the stomach using MRI and microbubble-based contrast agents
Dr. Richard Jiang is a Senior Lecturer (Associate Professor) in the School of Computing & Communications at Lancaster University, UK. He currently holds a prestigious Leverhulme Trust Research Fellowship. He is a Fellow of HEA, an Associate Member of EPSRC College, and an EPSRC RISE Connector.
Dr Jiang's research interest mainly resides in the fields of Biometrics, Privacy & Security, Intelligent Systems, and Biomedical Image Analysis. His recent research has been supported by grants from EPSRC (EP/P009727/1), Leverhulme Trust (RF-2019-492), Qatar National Research Fund (NPRP No.8–140-2–065) and other industry/international funders. He has supervised and co-supervised over 10 PhD students. He authored over 60 publications and is the lead editor of three Springer books. He served as a TPC member and a reviewer for various international conferences and journals.
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