Statistical machine learning for medical imaging

The project develops computationally statistical machine learning tools for a medical imaging application.

Start date

1 April 2023

Duration

4 years

Application deadline

03 March 2023

Funding source

Doctoral College Matched Studentship

Funding information

UK tuition fee
Enhanced UKRI stipend at £19,062 p.a. (2022/23 rate)
Research Training Support at £1,000 p.a.
Personal Computer (provided by the Department)

Supervised by

Dr Yunpeng Li

Visiting Reader

Dr Samaneh Kouchaki

Senior Lecturer in Machine Learning for Healthcare

About

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:

Data uncertainty integration for medical imaging
Model uncertainty quantification
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.

Additional notes

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 Computer 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.

Later start dates possible: July 2023.

Eligibility criteria

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).

This project is for UK and settled EU candidates only, only UK/EU settled fees covered.

English language requirements

IELTS minimum 6.5 overall with minimum 6.0 in each component, or equivalent.

How to apply

Applications should be submitted via the Computer Science PhD programme page on the "Apply" tab. Please clearly state the studentship title and supervisor on your application.