Keynote speakers of MICAD2021
Yale University, USA
IEEE Fellow, AIMBE Fellow; MICCAI Fellow
James S. Duncan is the Ebenezer K. Hunt Professor of Biomedical Engineering and a
Professor of Radiology & Biomedical Engineering, Electrical Engineering and
Statistics & Data Science at Yale University. Dr. Duncan received his B.S.E.E. with
honors from Lafayette College (1973), and his M.S. (1975) and Ph.D. (1982) both in
Electrical Engineering from the University of California, Los Angeles. Dr. Duncan has
been a Professor of Diagnostic Radiology and Electrical Engineering at Yale
University since 1983. He has been a Professor of Biomedical Engineering at Yale
University since 2003, and the Ebenezer K. Hunt Professor of Biomedical
Engineering at Yale University since 2007. He has served as the Acting Chair and is
currently Director of Undergraduate Studies for Biomedical Engineering. Dr.
Duncan’s research efforts have been in the areas of computer vision, image
processing, and medical imaging, with an emphasis on biomedical image analysis
and image-based machine learning. He has published over 280 peer-reviewed
articles in these areas and has been the principal investigator on a number of peerreviewed
grants from both the National Institutes of Health and the National Science
Foundation over the past 30 years. He is a Life Fellow of the Institute of Electrical
and Electronic Engineers (IEEE), and a Fellow of the American Institute for Medical
and Biological Engineering (AIMBE) and of the Medical Image Computing and
Computer Assisted Intervention (MICCAI) Society. In 2014 he was elected to the
Connecticut Academy of Science & Engineering. He has served as co-Editor-in-Chief
of Medical Image Analysis, as an Associate Editor of IEEE Transactions on Medical
Imaging, and on the editorial boards of Pattern Analysis and
Applications, the Journal of Mathematical Imaging and Vision, “Modeling in
Physiology” of The American Physiological Society and the Proceedings of the IEEE.
He is a past President of the MICCAI Society. In 2012, he was elected to the Council
of Distinguished Investigators, Academy of Radiology Research and in 2017
received the “Enduring Impact Award” from the MICCAI Society
Speech Title: Neuroimage Analysis in Autism: from Model-Based Estimation to Data-driven Learning
The Hebrew University of Jerusalem, Israel
IEEE Fellow, ASME Fellow; MICCAI Fellow
President of the MICCAI Society -- Medical Image Processing and Computer Aided Interventions
Leo Joskowicz is a Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel since 1995. He is the founder and director of the Computer-Aided Surgery and Medical Image Processing Laboratory (CASMIP Lab). Prof. Joskowicz is a Fellow of the IEEE, ASME, and MICCAI (Medical Image Processing and Computer Aided Intervention) Societies. He is the President of the MICCAI Society and was the Secretary General of the International Society of Computer Aided Orthopaedic Surgery (CAOS) and the International Society for Computer Assisted Surgery (ISCAS). He is the recipient of the 2010 Maurice E. Muller Award for Excellence in Computer Assisted Surgery by the International Society of Computer Aided Orthopaedic Surgery and the 2007 Kaye Innovation Award. He has published over 250 technical works including conference and journal papers, book chapters, and editorials and has 12 issued patents. He is on the Editorial Boards of six journals, including Medical Image Analysis, Int. J. of Computer Aided Surgery, Computer Aided Surgery, and Nature Scientific Reports and has served on numerous related program committees.
Speech Title: Accelerating Deep Learning Medical Image Analysis in Radiology
Abstract: Radiology, one of the cornerstones of modern healthcare, is undergoing rapid and profound changes due to the ever-increasing number of imaging examinations, the shortage of certified radiologists, the dynamics of healthcare economics, and the technological developments of artificial intelligence based image processing. This constellation has created unique opportunities for Computational Radiology, whose goal is to automatically extract meaningful radiomics features from medical images in support of clinical decision making. State-of-the-art methods for features extraction are based on deep learning classification algorithms that are starting to reach near human performance. However, developing deep learning methods requires large manually annotated datasets, which are seldom available and are expensive and time-consuming to create.
This talk will present an overview of our new methods for the fast development of deep learning-based image processing solutions in Radiology with very few annotated datasets. The key idea is to bootstrap the creation of expert-validated annotations with new techniques for annotation uncertainty estimation and for learning how experts correct annotations generated by deep learning networks initially trained with very few annotated datasets. Our methods aim to optimize radiologist time, reduce the annotated dataset size, and increase the accuracy and robustness of the deep neural networks results. We expect that our methods will significantly lower the entry cost, shorten the time and reduce the effort currently required to develop and deploy deep learning based solutions for radiology.
University of Leeds, UK
IEEE Fellow, SPIE Fellow
Diamond Jubilee Chair in Computational Medicine
Royal Academy of Engineering Chair in Emerging Technologies
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.
Speech Title: From medical image computing to In silico trials of medical devices
Traditional medical product development life-cycle begins with pre-clinical development. In laboratories, bench/in-vitro experiments establish plausibility for treatment efficacy. Then in-vivo animal models with different species guide medical device efficacy/safety for humans. With success in both in-vitro/in-vivo studies, a scientist can propose clinical trials testing whether the product is made available for humans. Clinical trials often involve testing across many people, which is costly, lengthy, and sometimes implausible (e.g. paediatric patients, on rare diseases, small ethnic groups). When medical devices fail at later stages, financial losses can be catastrophic (high-risk pre-market approval (PMA) device costs can average to £74m of which £54m are spent in FDA-linked regulatory stages over an average of 4.5 years). Many reports have pointed to this broken/slow innovation system, and its impact on societal costs and suboptimal healthcare but radical changes to this innovation process are still to be developed.
This talk introduces how computational imaging and computational modelling can deliver a paradigm shift in medical device innovation where quantitative sciences are exploited to carefully engineer device designs, explicitly optimise the clinical outcome, and thoroughly test side-effects before being marketed. In-silico clinical trials are essentially computer-based medical device trials performed on populations of virtual patients. They use computer models/simulations to conceive, develop and assess devices with the intended clinical outcome explicitly optimised from the outset (a-priori) instead of tested on humans (a-posteriori). This will include testing for potential risks to patients (side effects) exhaustively exploring in-silico for medical device failure modes and operational uncertainties before tested in live clinical trials.
We will explore this topic, give examples and signpost areas of further research where the medical image computing community can make a considerable contribution in combination with other convergent technologies.
The University of Iowa, IA, USA
IEEE Fellow, AIMBE Fellow,
Image Analysis Group Leader Iowa Institute for Biomedical Imaging
Co-Founder, VIDA Diagnostics, Inc., Iowa City, IA
Joseph M. Reinhardt is the Roy J. Carver Chair of Biomedical
Engineering at the University of Iowa. He received the BS degree from
Carnegie Mellon University, the MS degree from Northeastern
University, and the PhD degree from Penn State University, all in
Electrical Engineering. Dr. Reinhardt worked for several years in
industry as a radar systems engineer. He is currently Professor and
Department Executive Officer (chair) of the Roy J. Carver Department
of Biomedical Engineering. Dr. Reinhardt teaches courses in the areas
of computer programming, biomedical instrumentation, and medical
Dr. Reinhardt is a fellow of the Institute of Electrical and Electronic Engineers (IEEE) and a fellow of the American Institute of Medical and Biological Engineering (AIMBE). His research interests are in the area of medical image processing, with a special emphasis on pulmonary imaging. Dr. Reinhardt has received research support from the National Institutes of Health, National Science Foundation, the Roy J. Carver Charitable Trust, and the Whitaker Foundation.
Dr. Reinhardt, together with colleagues from The University of Iowa, founded VIDA Diagnostics, an Iowa-based medical imaging software company that focuses on computer-aided diagnosis and image-guided interventions for lung disease.
Speech Title: Lung Imaging and Machine Learning for Chronic Obstructive Pulmonary Disease
Abstract： Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the U.S. and is a serious health problem worldwide. COPD is a complex lung disease characterized by permanent airflow obstruction. COPD is often caused by smoking, but it can also occur due to environmental exposure or genetic factors. Computed tomography (CT) imaging can describe the spatial distribution of the disease and measure the extent of emphysema and airway disease in COPD. In this talk, I will describe how image-based features derived from lung tissue texture patterns and biomechanical measurements computed using image registration can improve our understanding of the normal and diseased lung and provide diagnostic information to help detect, stage, and predict the progression over time of diseases such as COPD.
Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA
Le Lu received an MSE in 2004 and a PhD in 2007 in Computer Science from Johns Hopkins University. Prior to and during his PhD, he completed two year-long internships at Microsoft Research. In 2006, he joined Siemens Corporate Research at Princeton, New Jersey as a research scientist. He eventually served both as a member of the Medical Solutions’ computer-aided diagnosis & Therapy group and as a senior staff scientist, until 2013. During his seven years at Siemens, he made significant contributions to the company’s CT colonography and Lung CAD product lines. From 2013 to 2017, Dr. Lu served as a staff scientist in the Radiology and Imaging Sciences department of the National Institutes of Health Clinical Center. He then went on to found Nvidia’s medical image analysis group, in which he held the position of senior research manager until June 2018. Since then, he has been the Executive Director at PAII Inc., Bethesda Research lab, Maryland, USA. Dr. Lu's research interests lie in medical image computing/analysis, statistical/deep learning, clinical informatics and novel imaging biomarkers in the areas of oncology, radiology, and discovery of cancer treatment solutions. He has published over 176 peer-reviewed journal/conference articles, 35 peer-reviewed clinical abstracts and 54 US/International patents, including 32 MICCAI papers, two of which have received the MICCAI Society Young Scientist Award runner-up (Harrison 2017) and Publication Impact Award (Roth 2018). Additionally, he was the main technical leader for two of the most-impactful public radiology image dataset releases (NIH ChestXray14, NIH Clinical Center Director’s Award; NIH DeepLesion 2018). He coauthored the highest-cited IEEE Trans. on Medical Imaging article and the highest-cited medical imaging CVPR paper in the last five years, with his collaborating postdoc fellows (Hoo-Chang Shin 2016, Xiaosong Wang 2017). Two of his publications received the RSNA Informatics Research Trainee Awards (Xiaosong Wang 2016, Ke Yan 2018). In addition to his extensive research and publication activities, Dr. Lu plays an active role in the leading societies of the computer vision and medical imaging fields. He is a long-standing member of the MICCAI Society, elevated IEEE Fellow class of 2021 for my contributions to machine learning for cancer detection and diagnosis, member of IEEE Signal Processing Society, and member of the IEEE Computer Society. He serves as an Associate Editor of IEEE Trans. on Pattern Analysis and Machine Intelligence, IEEE Signal Processing Journal. In 2017 and 2019, he co-edited two books on Deep Learning and Convolutional Neural Networks for Medical Image Computing by Springer-Nature. Dr. Lu was Area Chair for MICCAI in 2015, 2016, 2018 (having participated in most MICCAI conferences since 2011); IEEE CVPR in 2017, 2019, 2020, and 2021; and AAAI in 2019 and 2020, and won best reviewer awards at CVPR 2018, BMVC 2018 and NeurIPS 2020.
Speech Title: Multi-phase CT Imaging+AI Enabled Deep Precision Medicine Solutions for Pancreatic Cancer: Multi-institutional Screening, Precision Diagnosis and Prognosis
University of New South Wales, Australia
Erik Meijering is a Professor of Biomedical Image Computing at the University of New South Wales (UNSW) in Sydney, Australia. His research interests are in Computer Vision and Artificial Intelligence for Quantitative Biomedical Image Analysis, on which he has published more than 100 papers. He received his PhD degree in Medical Image Analysis from Utrecht University in 2000 and the MSc degree in Electrical Engineering from Delft University of Technology in 1996, both in the Netherlands. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and serves on the IEEE SPS Technical Committee on Bio Imaging and Signal Processing (BISP), the IEEE EMBS Technical Committee on Biomedical Imaging and Image Processing (BIIP), and the cross-Society IEEE Life Sciences Technical Community (LSTC). 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 and co-organized conferences in the field, notably the IEEE International Symposium on Biomedical Imaging (ISBI) and the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI). He also served/serves on a great variety of other international conference, advisory, and review boards.
Speech Title: Artificial Intelligence in Bioimage Analysis
To enable personalized medical healthcare, it is of key importance to understand the cellular and molecular mechanisms of life in health and disease. Advanced biomedical imaging technologies are having an enormous impact on research in this area, as they allow visualizing the structure and function of whole organisms, organs, tissues, cells, and even single molecules with very high sensitivity and specificity. They also facilitate the discovery of new biomarkers for early diagnosis and preclinical validation of novel treatments in tissue or animal models as a first step towards clinical implementation. However, biomedical imaging devices typically generate vast amounts of multiparametric spatiotemporal imaging data, containing much more relevant and subtle information than can be processed by humans, even if they are experts. Hence there is a growing need for computational methods to analyze these data automatically, not only to cope with the sheer volume of biomedical image data sets, but also to reach a higher level of accuracy, objectivity, and reproducibility. To this end we develop advanced computer vision methods for a wide range of problems, including restoration, enhancement, super-resolution, and registration of images, as well as detection, segmentation, quantification, classification, and tracking of objects in these images. And to cope with the complexity of these problems, we rely increasingly on machine learning approaches for this, in particular deep learning using artificial neural networks. In addition to developing new methods, we are strong proponents of evaluating and benchmarking methods thoroughly and making them publicly available in the form of user-friendly software tools. This talk will highlight methods we have been developing specifically for cell and particle tracking and motion analysis.
IBM Fellow, Chief Scientist, Medical Sieve Radiology Grand Challenge
Almaden Research Center, IBM
IEEE Fellow, AIMBE Fellow
Dr. Tanveer Syeda-Mahmood is an IBM Fellow and Chief Scientist/overall lead for the Medical Sieve Radiology Grand Challenge project in IBM Research, Almaden. Medical Sieve is an exploratory research project with global participation from many IBM Research Labs around the world including Almaden Labs in San Jose, CA, Haifa Research Labs in Israel and Melbourne Research Lab in Australia. The goal of this project is to develop automated radiology and cardiology assistants of the future that help clinicians in their decision making.
Currently, she is working on applications of content-based retrieval in healthcare and medical imaging. Over the past 30 years, her research interests have been in a variety of areas relating to artificial intelligence including computer vision, image and video databases, medical image analysis, bioinformatics, signal processing, document analysis, and distributed computing frameworks. She has over 200 refereed publications and over 80 patent filed.
Dr. Syeda-Mahmood will be the General Chair of MICCAI 2023, the premier conference in medical imaging. She was the General Chair of the First IEEE International Conference on Healthcare Informatics, Imaging,and Systems Biology, San Jose, CA 2011. She was also the program co-chair of CVPR 2008. Dr. Syeda-Mahmood is a Fellow of IEEE. She is also the first IBMer to become an AIMBE Fellow. She is also a member of IBM Academy of Technology. Dr. Syeda-Mahmood was declared Master Inventor in 2011 and in 2019. She is the recipient of key awards including IBM Corporate Award 2015, Best of IBM Award 2015, 2016 and several outstanding innovation awards.
Director ARTORG and Group Head
University of Bern, ARTORG Center for Biomedical Engineering Research, Switzerland
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.
Speech Title: Safe instrument detection during surgery
Surgical scenes provide a highly challenging context for computer vision. While constrained in space, surgery is a highly dynamic environment with complex geometry, textureless surfaces, intermittent smoke and blood, as well as extreme changes in focus and blur. In this context, automatic detection of surgical instruments plays an important role in the role out of surgical robotic systems, but also in large scale analytics and educational platforms for surgery. Yet, detecting surgical instruments reliably remains an overwhelming challenge. In this talk, we will discuss recent works from our group in this domain. In the first, we introduce a deep learning approach that probabilistically models the scene of instruments to not only classify instruments present, but also estimate their positions. We then follow on to show how instance detection can be achieved by levering segmentation and clustering. Last, we discuss the need for safe-guard methods that prevent deep learning models deployed in the field from evaluating images they ought not too. To combat this problem we discuss a recent out-of-distribution methods we have designed, which projects images from an unlabeled training set into a low-dimensions space by optimising a network to maximise the likelihood of the training data. We show that our approach is highly effective at finding images that should not be evaluated by a subsequently training method.
Head of Division, Division of Visual Information and Interaction
Uppsala University, Sweden
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.
Nagoya University, Japan
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).
VP Research, Zebra Medical Vision Ltd
Ayelet Akselrod-Ballin’s is the Vice President of Research at Zebra Medical Vision where she leads a group of AI researchers. With over 20 years of experience both in Academia and Industry focusing on novel technologies for computer vision, machine learning, deep learning, and natural language processes applied to healthcare. Ayelet did her Post-doctoral research as a fellow in the Computational Radiology Laboratory at Harvard Medical School, Children’s Hospital (Boston) and she holds a Ph.D. in Applied Mathematics and Computer Science from Weizmann Institute of Science. Prior to joining Zebra Medical Vision, Ayelet led the medical imaging research technology at IBM-research and led the Computer Vision & Algorithms team at the MOD.
Speech Title: Deep Learning Solutions for Real World Healthcare Applications
co-founder and the CTO of Viz.ai
David Golan is a co-founder and the CTO of Viz.ai - a digital healthcare company harnessing deep learning
to analyze medical data and improve clinical workflow.
Viz.ai developed the First ever FDA-approved AI-powered triage system for stroke.
Prior to founding Viz.ai, David was a Fulbright post-doctoral scholar at Stanford university, working on
leveraging deep learning for the analysis of medical imaging and genetic data.
David holds a PhD in Statistics and Machine learning from Tel-Aviv University, and have coauthored more
than 20 scientific papers including three publications in the journal Science.
Prior to his academic career, David founded the ML team of b-hive Networks, an Israeli startup which was
acquired by VMWare in 2008
Speech Title: Saving lives at Viz.ai
Keynote speakers of MICAD2020 ：
Prof. Yu-Dong Zhang
University of Leicester, UK
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
Prof. Manuchehr Soleimani
University of Bath, UK
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
City, University of London, UK
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
Lancaster University, UK
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.