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Machine learning for the quantitative analysis of medical images 12 Sep 2017

There is a great deal of excitement and controversy in the potential of machine learning (ML) and artificial intelligence (AI) applied to healthcare. Will AI/ML cure cancer? Will it replace doctors? In this session, Dr Timor Kadir will discuss some of the real world applications of such technologies and go into some depth on two particular areas: stratification of patients with possible lung cancer and management of patients with chronic back pain. Both will be based around medical imaging and he will discuss what ML/AI can provide beyond what has been possible to date and its impact on the role of clinicians. Dr Rajarshi Banerjee's will discuss the use of quantitative MRI and big data to improve the care of patients with liver disease. 

1 CPD credit.
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Duration:85 mins


Speaker info

Dr Timor Kadir

Dr Timor Kadir graduated with an MEng in Electrical and Electronic Engineering from Surrey University in 1996 and studied for a DPhil at University of Oxford under Sir Michael Brady. He joined CTI/Siemens as a Research Scientist working on computer vision software and in 2009, during a management buy-out, Timor became CTO for Mirada Medical. Most recently, he founded Optellum, a company delivering machine learning based clinical risk stratification. He is currently the CTO of Optellum, Mirada and visiting fellow at Oxford. He’s published a reasonable number of papers, filed a bunch of patents and his h-index isn’t bad either.

Dr Rajarshi Banerjee

Dr Rajarshi Banerjee is CEO of Perspectum, and developed the multi-parametric liver MRI techniques at the University of Oxford, which led to the creation of Perspectum Diagnostics. He is an expert on liver imaging and a consultant physician for Oxford University Hospitals NHS Foundation Trust. Dr Banerjee has pioneered quantitative liver imaging for clinical use. His medical research work in liver disease and extensive experience in running clinical trials whilst working in multidisciplinary scientific teams to develop applications for cutting edge imaging technology means he is ideally placed to lead innovation in his field.