School of Mathematics, Computer Science and Engineering, City, University of London and the Institute of Physics and Engineering in Medicine (IPEM), UK
Over the decades the contribution of Science, Technology, Engineering, and Mathematics (STEM), in healthcare has been meteoric. STEM experts in partnership with healthcare professionals have been at the forefront of innovation, contributing significantly in tackling global health challenges. A perfect example of this is the clinical engineering contribution to the NHS during Covid-19. The nature of the contribution of STEM in healthcare is multifaceted. These, amongst many, include contribution to new knowledge relating to disease; innovation of new technologies for the better monitoring and consequently diagnoses and treatment of disease improving patient outcomes; novel and intelligent processes enabling a better and safer running of our National Healthcare System, etc.
The primary motivation of these seminar series is to expose and stimulate our community with some of the most exciting and cutting-edge research and innovation of STEM in healthcare.
The webinar series presented in 2021 will include:
11:00 – 11:02 Introduction and Opening Remarks (Chair)
11:02 – 11:17 Constantino Carlos Reyes-Aldasoro, “Analysis of cancer cells with Artificial Intelligence and Human Intelligence”
11:17 – 11:32 Christina Malamateniou, “AI in Radiography, the beauty is in the AI of the beholder”
11:32 - 11:47 Giacomo Tarroni, “Learning to tell wrong from right: detecting anomalies in medical images without expert supervision”
11:47 – 12:00 Q & A session (Chair)
Webinar chair: Dr Eduardo Alonso, Director Artificial Intelligence Research Centre (CitAI), City, University of London
Dr Constantino Carlos Reyes-Aldasoro
Senior Lecturer School of Mathematics, Computer Science and Engineering, City, University of London
Dr Reyes-Aldasoro is an interdisciplinary scientist with interest in Computer Science, Engineering and Life Sciences, in particular Cancer and Microcirculation. He has a degree in Mechanical and Electrical Engineering (UNAM, Mexico), MSc in Electrical Engineering (Imperial College) and PhD in Computer Science (Warwick) and worked as a Research Associate and Fellow at the Department of Surgical Oncology, School of Medicine of The University of Sheffield. He is currently Senior Lecturer in Computer Science in the Department of Computer Science at City, University of London.
Dr Reyes-Aldasoro is author of one book on Image Processing with Matlab (Wiley), has published more than 50 peer-reviewed journal papers, numerous conference papers and edited several conference proceedings and special issues in prestigious journals like Medical Image Analysis. He is academic editor of PLoS ONE, Immuno-Informatics and previously of Oncology News and has been guest editor of several journal special issues (BACR, MIUA, IET), reviewed for many journals and act as Editor (PLOS ONE, Oncology News) and Guest Editor (IET, Medical Image Analysis).
In teaching, I have delivered many courses at undergraduate, postgraduate and continuous education levels in the areas of Computer Science, Engineering, Physics and Medicine. .
Dr Christina Malamateniou, PhD MA (Cl. Ed) MAcadMEd DIC BSc (Hons) SFHEA
Director of the Postgraduate Programme in Radiography, Division of Radiography and Midwifery School of Health Sciences, City, University of London
Christina Malamateniou was awarded her BSc (Hons) in radiography from the Technological Institute of Athens (2002), MA in Clinical Education in 2019 form King’s College London and PhD in Perinatal Imaging in 2007 from Imperial College London.
Christina Malamateniou is the postgraduate programme director in Radiography at City, University of London and leads research in designing and validating AI tools for MRI practice, patient centred care and MR image optimisation. She has also designed and implemented the first postgraduate modules for radiographers in Artificial Intelligence and patient centred care in the EMEA region. Her funded research also includes projects on autism friendly MRI and the impact of Covid19 on the radiography workforce as well as research mentoring for radiographers. She supervises PhD and master’s students in the following topics: AI in radiography, Patient centred care in medical imaging and fetal MRI. Her Covid19 paper published in Radiography journal has become the most viewed paper with more than 1000 views. She has run the first Ai Conference for radiographers in July 2020 with more than 600 participants from 45 countries worldwide.
Dr Malamateniou has received funding as PI or Co-I in the excess of £2,800,000 by the NIH, NIHR, Society and College of Radiographers, BRC and GCRF. She has received the 2006 UKRC Leonard Levy Memorial prize and Clinical MRI prize by the British Institute of Radiology for her MRI research. She is a Senior Fellow of the Higher Education Academy as of July 2018. Since September 2020 she is also chairing the AI working group of the Society and College of Radiographers, is the vice chair of the Research Committee of the EFRS and a member of different AI standards committees, including in the BSI.
Dr Giacomo Tarroni
Lecturer in Artificial Intelligence, CitAI Research Centre, School of Mathematics, Computer Science and Engineering, City, University of London
Giacomo Tarroni has been a full-time researcher in the field of medical image analysis since 2009. His work has been mainly focused on image segmentation, image registration, quality control and object tracking for cardiovascular, brain and fetal images. In 2012, he obtained his Ph.D. from the University of Bologna, Italy (in collaboration with the University of Chicago, U.S.) working on the automated analysis of first-pass myocardial perfusion sequences in MRI. During his post-doc at the University of Padova, Italy, he focused on the automated analysis of fetal ultrasound images. After being awarded a Marie Skłodowska-Curie Fellowship, he moved to Imperial College London in 2015, where he became interested in the applications of machine learning and AI to automated organ detection, quality control assessment and motion correction for cardiac MRI. While still holding an Honorary Research Fellow position at Imperial College, since 2019 he his a Lecturer in Artificial Intelligence at City, and a core member of the CitAI Research Centre.
His current research focus is on machine learning for unsupervised anomaly detection in medical images, self-supervised approaches to medical image segmentation and federated learning in medical imaging.