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Machine learning algorithms are starting to become more widely implemented in a variety of software applications within radiology and radiotherapy. However, there remains a shortage of healthcare professionals with an understanding of such technologies, particularly in relation to their advantages and limitations.
This introductory, educational event is designed to increase the knowledge level of attendees such that more informed decisions can be made with regards implementation of machine learning tools in a clinical setting, whether purchased ‘off-the shelf’ or developed locally.
The event will provide a broad, high-level overview of key machine learning theories, with a focus on imaging and radiotherapy applications. Important issues related to clinical translation will also be discussed. Although no prior machine learning knowledge will be assumed, the event will briefly cover basic mathematical concepts. Therefore, a rudimentary understanding of statistics and linear algebra would be beneficial for getting the most out of the taught content.
Speakers:
- Dr Jonathan Taylor, Principal Clinical Scientist, Nuclear Medicine & 3DLab, Sheffield Teaching Hospitals
- Dr Sarah Gulliford, Senior Proton Beam Therapy Physicist, University College London Hospitals NHS Foundation Trust
- Dr Zach Eaton-Rosen, Research Scientist, Graphcore
- Dr James Leighs, Senior Clinical Scientist & Software Engineer, University Hospitals Southampton
- Mr Mark Graham, Research Associate, Kings College London
- Dr Shah Islam, Senior Consultant - Clinical, Hardian Health
3 CPD point
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