Fudan-KCL Virtual Workshop：Medical AI, Imaging and Robotics
Fudan
KCL
Introduction
There are increasing interests in educational and research collaborations between Fudan University and King’s College London with a memorandum of understanding signed between the two prestigious institutions in 2017.
The primary aim of this workshop is to promote collaborations between Institute of Science and Technology for Brain-inspired Intelligence (ISTBI) at Fudan and School of Biomedical Engineering and Imaging Sciences (BMEIS) at King’s by bringing together researchers from both institutions to showcase research activities in Medical AI, Imaging and Robotics.
Instructions for participants
SUMMER OF 2022
Date：Friday, 13 May 2022
Time：16:00-19:25 Beijing Time；09:00-12:25 London Time
Zoom Meeting ID：928 5012 9913
Password：212121
https://zoom.us/j/92850129913?pwd=eEZUd0VZMWdzNklZOE9jMXEzeDlmUT09
Co-Chairs:
Prof Wang He, Young Principal Investigator & Director of Zhangjiang International Imaging Centre, Institute of Science and Technology for Brain-Inspired Intellignece, Fudan University
Dr Xia Wenfeng, Lecturer and International Lead, School of Biomedical Engineering & Imaging Sciences, King’s College London
SCHEDULE
Agenda of the Forum
OPENING
Time：16:00 - 16:20 Beijing
09:00 - 09:20 London（20 minutes）
👏 Welcome to Meeting
Prof Wang Shouyan, Deputy Dean, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Prof Sebastien Ourselin, Head of School of Biomedical Engineering & Imaging Sciences, King’s College London
Prof Gan Zhongxue, Dean, Institute of Intelligent Robotics, Fudan University
Prof Francesco Dazzi, Vice Dean and Head of Regenerative Medicine, Faculty of Life Sciences & Medicine, King’s College London
PRESENTATIONS
16:20-16:40 Beijing；09:20-09:40 London
1. From brain data to brain simulation
Prof Feng Jianfeng, Dean, Institute of Science and Technology for Brain-Inspired Intellignece & School of Data Science, Fudan University
16:40-17:00 Beijing；09:40-10:00 London
2.Artificial intelligence for fetal and neonatal MRI
Dr Maria Deprez, Senior Lecturer in Medical Imaging, School of Biomedical Engineering & Imaging Sciences, King’s College London
17:00-17:20 Beijing；10:00-10:20 London
3. AI Neuropathologist for Brain Tumour Identification
Prof Wu Jingsong, Professor & Chief Physician in Neurosurgery, Huashan Hospital, Fudan University
17:20-17:40 Beijing；10:20-10:40 London
4. Learning from 16 million patients: scaling healthcare AI
Dr Jorge Cardoso, Reader of Artificial Medical Intelligence, King’s College London
17:40-18:00 Beijing；10:40-11:00 London
5. Intelligent diagnosis based on multimodal medical images
Prof Yu Jinhua, Professor, Department of Electronic Engineering, Fudan University
18:00-18:20 Beijing；11:00-11:20 London
6. Image-guided robotic retinal surgery
Dr Christos Bergeles, Reader of Surgical Robotics, School of Biomedical Engineering & Imaging Sciences, King’s College London
18:20-18:40 Beijing；11:20-11:40 London
7. Imaging-based quantification of vascular morphology and flow
Prof Wang He, Professor & Director of Zhangjiang International Imaging Centre, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
18:40-19:00 Beijing；11:40-12:00 London
8. Packing sound and light into a smart needle for surgical guidance
Dr Xia Wenfeng, Lecturer and International Lead, School of Biomedical Engineering & Imaging Sciences, King’s College London
CONCLUSION
Time：19:00 - 19:20 Beijing
12:00 - 12:20 London（20 minutes）
Discussion
All participants
Time：19:20 - 19:25 Beijing
12:20 - 12:25 London（20 minutes）
Conclusion
Prof Wang Shouyan, Deputy Dean, Institute of Science and Technology for Brain-inspired Intelligence, Fudan University
Prof Francesco Dazzi, Vice Dean and Head of Regenerative Medicine, Faculty of Life Sciences & Medicine, King’s College London
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Fudan University
Prof. Wang Shouyan |
Deputy Dean & Research Professor, Institute of Science and Technology for Brain-Inspired Intellignece, Fudan University
Research Interests:
· Digital Medicine & Therapeutics
· Intelligent Brain Stimulation
· Neural Decoding
Email: shouyan@fudan.edu.cn
Biography: Professor Shouyan Wang received the BSc degree in Biomedical Engineering in 1994, and the Msc and PHD degree in Physiology from the Fourth Military Medical University, Xi’an, China, in 1997 and 2000, respectively. He was a Postdoctoral Research Fellow in the Neurosurgery Department of JR Hospital and Department of Physiology, Anatomy and Genetics at University of Oxford from 2002 to 2007.
He worked as a Lecturer in the institute of Sound and Vibration Research at University of Southampton from 2007 to 2012. He became a Professor at Suzhou Institute of Biomedical Engineering and Technology of Chinese Academy of Sciences, and the Directors of the Biomedical Electronics Department, Key Lab of Neural Engineering and Technology at Suzhou from 2012 to 2017. He joined Fudan University in 2017 and has been the Director of Neural and Intelligent Engineering Centre at ISTBI.
His research focuses on the intelligent neuromodulation of deep brain stimulation for neurological diseases, including identification of biomarkers from human deep brain local field potentials, technology development of miniaturized adaptive electrical or optical stimulator, and monitoring of motor or sensory behaviors with wearables devices. Professor Shouyan Wang works toward integrating engineering, neuroscience, neurology and neurosurgery to advance the translational research from deep brain stimulation technology to neurological disease treatment.
Prof. Gan Zhongxue |
Dean, Institute of Intelligent Robotics, Fudan University
Research Interests:
· Flexible automatic control of robot
· Multi-agent behavior coordination and control
· Swarm intelligence
Email: ganzhongxue@fudan.edu.cn
Biography: Professor Zhongxue Gan received Ph.D. degree in mechanical engineering from University of Connecticut, Connecticut, USA, in 1993. He was a research fellow of ABB company during 1990 to 2005, and he was promoted to Chief scientist of ABB Global Robotics and flexible Automation in 2002. He returned to China in 2004 and was one of Distinguish Experts in the first batch of “Thousand Talents Program”.
Since 2017, He has served as Distinguished Professor in Fudan University, and been the dean of Institute of AI&Robotics, Fudan University. As the chief scientist of National “973 Plan” program and the expert of “12th Five-Year”“863 Plan” Program , Professor Gan has been making great contributions to the development of science and technology in China. He is the author of more than forty papers, three books and around 60 patents from USA and China. In 2010, he was awarded 'China International Science and Technology Cooperation Award' by Ministry of Science and Technology of the People's Republic of China.
His research focuses on the intelligent robotics, including and flexible automatic control, multi-agent behavior coordination and intelligent control, and swarm intelligence in heterogeneous space.
Prof. Feng Jianfeng |
Dean, Institute of Science and Technology for Brain-Inspired Intellignece & School of Data Science, Fudan University
Research Interests:
· Brain Diseases: Brain Disorders and Brain Tumour
· Computational Neuroscience: modelling, data analysis and experiment
· Machine Learning
Email: jffeng@fudan.edu.cn
Biography: Professor Feng is Chair Professor at Shanghai National Centre for Mathematic Sciences, Dean of Institute of Science and Technology for Brain-inspired Intelligence and Dean of School of Data Science at Fudan University. He has been developing new mathematical, statistical and computational theories and methods to meet the challenges raised in Brain Science and mental health researches. Recently, his research interests are mainly in big data analysis, mining for neuroscience and brain diseases and developing brain-inspired algorithms and theory.
He was awarded the Royal Society Wolfson Research Merit Award in 2011, as a scientist ‘being of great achievements or potentials’. He has made considerable contributions on modelling single neurons and neuronal networks, machine learning, and causality analysis with publications on JAMA Psychiatry, Molecular Psychiatry, Nature Human Behaviour, Science Advances, Brain, PNAS, PRL, IEEE TPAMI etc. He has proposed and developed nonlinear causality analysis, and successfully applied it to search the roots in depression, schizophrenia and autism, including the successful treatment of depressions. He was invited to deliver the 2019 Paykel Lecture at the University of Cambridge.
Professor Feng was recognized as one of the Chinese Most Cited Researchers in Neuroscience of 2019 and one of the Chinese Most Cited Researchers in Mathematics of 2020 and 2021 by Elsevier. He was also named the 2020 World’s Top 2% Scientists by Stanford University.
Prof. Wu Jingsong |
Professor & Chief Physician in Neurosurgery, Huashan Hospital, Fudan University
Research Interests:
· Comprehensive functional brain imaging
· Accurate neurosurgery
· Clinical brain tumor research
Email: wujinsong@huashan.org.cn
Biography:
Chief Physician
Professor, Fudan University
Doctoral Supervisor
Deputy Director, Neurosurgical Institute of Fudan University
Deputy Director of Glioma Center, Huashan Hospital
Director, Brain Function Laboratory, Deputy Director of Brain Biobank, Neurosurgical Institute of Fudan University
Chairman of Neurooncology Committee of Shanghai Anti-Cancer Association
Vice-Chairman of Neurooncology Committee of China Anticancer Association
Member of the ASNO Scientific Committee
Member of the WFANO Education Committee
Prof. Yu Jinhua |
Professor, Biomedical Engineering Center, Fudan University
Research Interests:
· Medical image analysis (Deep Learning, Radiomics, Medical big data)
· Ultrasound imaging (Imaging theory, Image method, Nonlinear ultrasound)
· Computer aided diagnosis
Email: jhyu@fudan.edu.cn
Biography:Prof. Jinhua Yu received her PhD degree from the School of Information Science and Engineering, Fudan University in 2008, specializing in biomedical engineering. From 2008 to 2010, she worked as a postdoctoral researcher in the Department of Biomedical Engineering at the University of Missouri, U.S.A. She joined the Centre for Biomedical Engineering at Fudan University as an associate researcher at the end of 2010 and was promoted to Professor at the end of 2015.
She is mainly engaged in research on medical image processing, medical image big data, artificial intelligence, and computer-aided diagnosis. She has published more than 100 peer-reviewed papers and led several national and provincial level research projects such as the Major Research Program of National Natural Science Foundation.
She was awarded the International Radio Union Young Scientist Award, the Highly Cited Paper Award in European Radiology, the Shanghai Natural Science Award, and the First Prize of the China Biomedical Engineering Competition. She is a deputy director of the Chinese Committee of Instrumentation and member of several academic Committees, including the Chinese Committee of Biomedical Engineering, the Chinese Committee of Acoustics, the Committee of Women in Science and Technology of the Chinese Biomedical Engineering, and the Shanghai Committee of Biomedical Engineering.
Prof. Wang He |
Acting Director, Zhangjiang International Imaging Centre (ZIC)
Associate Professor, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University
Research Interests:
· Magnetic Resonance Imaging Method
· computer-aided diagnosis
· Cerebral Vascular Disease
Email: hewang@fudan.edu.cn
Biography: Dr. He Wang is an Associate Professor/Principle Investigator at Fudan University, Institute of Science and Technology for Brain-Inspired Intelligence (ISTBI) since 2016. After receiving B.S. and Ph.d. in Physics at East China Normal University, Dr. Wang performed his postdoc work with Dr. Xiaoping Hu at Emory. In the later 8 years, he worked for GE and Philips, where his work was focused on MR pulse sequence design, image reconstruction and image post processing.
He has 20 years expertise in magnetic resonance imaging and cooperated with more than 30 top hospitals of China for many years. He is currently in charge of the projects including vascular morphology and flow of cerebral small vessel disease, intelligent diagnosis of breast and liver cancer, and development of magnetic resonance sequence for animal scanner.
King’s College London
Prof. Sebastien Ourselin |
Professor of Healthcare Engineering and Head of School, School of Biomedical Engineering & Imaging Sciences, King’s College London
Research Interests:
· Biomedical and life sciences
· Computer science
Email: sebastien.ourselin@kcl.ac.uk
Biography:Professor Sebastien Ourselin is Head of the School of Biomedical Engineering & Imaging Sciences, King’s College London and Deputy Director of the London Medical Imaging and Artificial Intelligence Centre for Value Based Healthcare. Prior to this, he was at University College London where he served as Vice-Dean for Health (Faculty of Engineering) and Director of the Institute of Healthcare Engineering.
He has over 20 years of experience within academia and research organisations across three countries. Alongside Guy’s & St Thomas’ NHS Foundation Trust, he is leading the establishment of a MedTech Hub, located at St Thomas’ campus. The vision for the Hub is to create a unique infrastructure, enabling academia, industry and the NHS to work in synergy and develop health technologies (including medical devices), workforce and operational improvements that will be of global significance.
He has significant experience in translating and commercialising healthcare technology and is a co-founding member of two academics pin-out companies, BrainMiner Ltd (which utilises machine learning algorithms for brain image analysis) and Hypervision Surgical Ltd (which aims to deliver artificial intelligence-enabled hyperspectral imaging in the operating room).
Over the last 20 years, he has raised over £60M as Principal Investigator and has published over 550 articles (over 42451 citations, h-index 101). He is an elected Fellow of the Royal Academy of Engineering (FREng,2021), Fellow of the Institute of Physics and Engineering in Medicine (FIPEM,2021) and Fellow of the Medical Image Computing and Computer Assisted Intervention Society in (FMICCAI,2016).
Prof. Francesco Dazzi |
Vice Dean (International) and Head of Regenerative Medicine, Faculty of Life Sciences & Medicine, King’s College London
Email: francesco.dazzi@kcl.ac.uk
Biography: Professor Francesco Dazzi has been working on the biology and clinical applications of cellular therapies in haemopoietic stem cell transplantation for the last 20 years. He obtained an MD and a PhD at Padua University Medical School (Italy) and subsequently trained as a Haematologist at Verona University and at the Royal Postgraduate Medical School (London, UK). He was appointed Senior Lecturer and then Professor in Stem Cell Biology at Imperial College in 2005. In 2014 he moved to King’s College London where he is Professor of Regenerative & Haematological Medicine and leads Cellular Therapies for Kings’ Health Partners.
Francesco pioneered a large and highly successful cellular immunotherapy programme for leukaemia and characterised the immunosuppressive effects of mesenchymal stromal cells (MSC). His team successfully tested MSC in pre-clinical models and the work has formed the basis of UK wide clinical studies.
Professor Dazzi was appointed to the role of Vice-Dean (International) for the Faculty of Life Sciences & Medicine in 2019.
Dr. Maria Deprez |
Senior Lecturer in Medical Imaging, School of Biomedical Engineering & Imaging Sciences, King’s College London
Research Interests:
· Artificial intelligence for motion correction, image analysis, diagnosis and prognosis using fetal MRI, with focus on normal development and congenital anomalies
· Artificial intelligence for image analysis and biomarker extraction for MRI of the developing brain, in conditions such as preterm birth, autism spectrum disorder or schizophrenia.
Email: maria.deprez@kcl.ac.uk
Biography: Maria Deprez is a Senior Lecturer in Medical Imaging at King’s College London. She leads research on motion correction and image analysis of fetal and neonatal MRI to enable diagnosis and biomarker extraction. Her techniques are underpinning numerous fetal research projects and fetal MRI clinical services at St Thomas’ and Evelina Children Hospital London.
Dr Deprez has over 15 years of experience in advanced image analysis with focus on MRI during fetal, neonatal and early childhood periods. She completed her PhD in Medical Image Computing at Imperial College London in 2009. She worked as a postdoctoral researcher at Imperial College London, University of Oxford and King’s College London, before taking up a lectureship position at King’s.
Dr. Jorge Cardoso |
Reader of Artificial Medical Intelligence, King’s College London
Research Interests:
· AI in Medical Imaging
Email: m.jorge.cardoso@kcl.ac.uk
Biography:M Jorge Cardoso is Reader in Artificial Medical Intelligence at King’s College London, where he leads a research portfolio on big data analytics, quantitative radiology and value-based healthcare. Jorge is also the CTO of the new London Medical Imaging and AI Centre for Value-based Healthcare.
He has more than 12 years expertise in advanced image analysis, big data, and artificial intelligence, and co-leads the development of project MONAI, a deep-learning platform for artificial intelligence in medical imaging. He is also a founder of BrainMiner and Elaitra, two medtech startups aiming improve neurological care and breast cancer diagnosis, respectively.
Dr. Christos Bergeles |
Reader of Surgical Robotics, School of Biomedical Engineering & Imaging Sciences, King’s College London
Research Interests:
· Surgical Robotic Systems
· Interventional Visual Servoing
· Robot Modelling and Control
Email: christos.bergeles@kcl.ac.uk
Biography:Christos Bergeles is Reader of Surgical Robotics at the School of Biomedical Engineering & Imaging Sciences of King’s College London. His research interests include micro-surgical robotics and interventional visual servoing. He leads the Robotics and Vision in Medicine (RViM) Lab, which develops robotic systems and multi-sensory guidance algorithms to achieve impossible interventions deep inside the human body. Dr. Bergeles received the Fight for Sight Award in 2014, and the ERC Starting Grant in 2016.
Christos received the M.Sc. Degree from the National Technical Univesity of Athens, and the Ph.D. degree in Robotics from ETH Zurich, Switzerland, in 2011. He was a postdoctoral research fellow at Boston Children’s Hospital, Harvard Medical School, Massachusetts, and the Hamlyn Centre for Robotic Surgery, Imperial College, United Kingdom. Further, he was an Assistant Professor at the Wellcome/EPSRC Centre for Interventional and Surgical Sciences of University College London.
Dr. Xia Wenfeng |
Lecturer and International Lead, School of Biomedical Engineering & Imaging Sciences, King’s College London
Research Interests:
· Photoacoustic imaging
· Minimally invasive imaging and sensing
· Medical device tracking and navigation
Email: wenfeng.xia@kcl.ac.uk
Biography: Dr. Wenfeng Xia received a BSc in Electrical Engineering from Shanghai Jiao Tong University, China, and a MSc in Medical Physics from University of Heidelberg, Germany, in 2005 and 2007, respectively. In 2013, he obtained his Ph.D from University of Twente, Netherlands. From 2014 to 2018, he was a Research Associate / Senior Research Associate in the Department of Medical Physics and Biomedical Engineering at University College London, UK.
He leads the Photons+ Ultrasound Research Laboratory (PURL) at King's College London, which brings together talented scientists who are committed to transform the ways that surgical and interventional procedures are performed via ground-breaking technological innovations. They are fascinated by how light and sound interact with biological tissue, and how they can be used for patient benefit. In particular, the research at PURL is centered on the technological advancements and clinical translation of photoacoustics (also called optoacoustics), an emerging imaging and sensing technique that is based on light generated ultrasound.
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