Cambridge Wireless: Deep Learning in Medical Imaging
Posted on 11/10/2018
Imaging for clinical interpretation or intervention is a key element of medicine, and there is currently a rapid growth in the number of medical imaging studies. The main challenge for clinicians is to interpret the complexity and dynamic changes of these images. Medical image computing has emerged as an interdisciplinary field to develop robust and accurate computational methods to extract clinically relevant information.
Recent advances in deep learning show that computers can extract more information from images, with an increase in reliability and accuracy. Moreover, deep learning can be used to identify and extract novel features that would otherwise not be easily accessible to human viewers. The major challenge is now to develop and adapt these techniques to enhance computer-aided detection and diagnosis, image analysis with deep learning to enhance interventions, integration of imaging and clinical data, as well as end-to-end learning for prognosis and treatment selection.
This event will allow you to find out whether recent advances in deep learning, through which computers are now able to extract information from images reliably and accurately, can be applied to the field of diagnostic imaging. The event will explore the challenges and how adapting these techniques can enhance detection, diagnosis, prognosis and treatment.
- Rajesh Jena, Radiation Oncology Consultant, University of Cambridge: Discussing Project Innereye at Microsoft Research
- Prof Fiona Gilbert, Professor of Radiology, University of Cambridge: Discussing the future of AI in breast imaging
- Dr Antony Rix, Co-founder and CEO, Granta Innovation: Discussing ‘Putting AI into production for medical diagnostics’
- Dr Hugh Harvey, Clinical Director, Kheiron Medical Technologies: Discussing ‘Perspectives on the challenges in developing deep learning algorithms for breast screening’
- Nigel Whittle, Head of Medical and Healthcare at Plextek
- Gabriela Juarez Martinez, Knowledge Transfer Manager for Life Sciences, Knowledge Transfer Network
To find out more and to register please click here.
If you book online then when you are going through the booking process enter this code HCS100 it will reduce the fee to zero. If you have any questions then please contact firstname.lastname@example.org