This project aims to develop novel uncertainty methodologies and a software toolkit with uncertainty awareness for radiograph-based disease detection. The proposed models are expected to differentiate themselves with the uncertainty quantification algorithms for the known uncertainty sources, as well as from unknown sources. This will be achieved through three concrete and actionable research tasks in the duration of the studentship: 1) data uncertainty integration for medical imaging; 2) model uncertainty quantification; and 3) clinician-in-the-loop AI (incorporating knowledge into the AI). Thus, in identified high uncertainty cases, human validation, intervention, and more extensive tests can be carried out to avoid potential error. The resulted open-source software toolkit will be validated through an AI-assisted radiograph-based dental disease detection application and is transferable across a wide range of diagnostic radiology applications.
The application is rolling-based with no fixed submission deadline until the position is filled. Early applications are strongly encouraged for early PhD start. The PhD student will be based at the Nature Inspired Computing and Engineering (NICE) research group in the Department of Compute Science at the University of Surrey. The student will also benefit from ample computing and research resources from Centre for Vision, Speech and Signal Processing (CVSSP) and the Surrey Institute for People-Centred AI.
Supervisors: Dr Yunpeng Li, Dr Samaneh Kouchaki.
Entry requirements
Open to UK students starting in April 2023. A later start date of July 2023 is also possible.
A Bachelor’s degree or above in Computer Science, Electrical Engineering, Statistics, Mathematics, Physics or similar (a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university).
English language requirements: IELTS Academic 6.5 or above (or equivalent) with 6.0 in each individual category, or equivalent. More about our English language requirements.
How to apply
Apply via the Computer Science PhD programme page.
Please clearly state the studentship title and supervisor on your application.
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