Next Generation Machine Intelligence for Medical Image Representation and Analysis
Medical imaging is a key technology in clinical routine for diagnosing disease, guiding interventions, and assessing the efficacy of treatment. Pathological structures can be made visible in high-resolution, three-dimensional images acquired with state-of-the-art techniques such as magnetic resonance imaging and computed tomography. Clinical experts need to undergo long training to be able to read these images and to learn to spot subtle signs of disease. With an increasing complexity and large volume of data, however, the interpretation of medical images and extraction of clinically useful information push human abilities to the limit. Without the support from intelligent computational tools, there is high risk that critical patterns of disease go undetected with possibly life-threatening consequences for patients and huge burden on society.
This project is devoted to the development of new intelligent computational tools using the power of machine learning for automated image analysis. The objective is to build robust, reliable and trustworthy algorithms to optimally support experts when making clinical decisions by extracting accurate, quantitative measurements from medical imaging data. The ultimate goal is to deliver the next generation machine intelligence to tackle major clinical challenges such as early detection of disease and gaining new insights about complex pathology.
The successful integration of AI for tasks such as image-based diagnostics has the potential to transform healthcare at many levels. AI could directly help to make better clinical care more widely accessible and reduce the socio-economic burden on overstretched healthcare systems. Artificial intelligence has already become a key pillar of a new global economy and a driver for creating thousands of jobs across Europe. Large European initiatives have been launched which promote the safe and ethical use of AI and this project aims to contribute towards the success of this ambition.
Ben Glocker is Reader in Machine Learning for Imaging at the Department of Computing at Imperial College London where he co-leads the Biomedical Image Analysis Group with more than 45 research staff. He also leads the HeartFlow-Imperial Research Team and is scientific advisor for Kheiron Medical Technologies and a Visiting Researcher at Microsoft Research Cambridge. He holds a PhD from TU Munich and was a postdoc at Microsoft and a Research Fellow at the University of Cambridge. His research is at the intersection of medical imaging and artificial intelligence aiming to build computational tools for improving image-based detection and diagnosis of disease. He has received several awards including a Philips Impact Award, a Medical Image Analysis – MICCAI Best Paper Award, and the Francois Erbsmann Prize. He is a member of the Young Scientists Community of the World Economic Forum and a member of the AI Task Group of the UK National Screening Committee advising the Government on questions around clinical deployment of AI for screening programmes.
Start date: 01 February 2018
End date: 31 January 2024
Overall budget: € 1 499 292
Host institution: Imperial College London
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