|Funding for:||UK Students|
|Funding amount:||The studentship is fully funded by EPSRC iCASE voucher number 220022 and NPL top-up of £29,621; stipend: £15,609/year plus living allowance top up of £2,991/year = Total: £18,600/year|
|Placed On:||27th January 2022|
|Closes:||1st July 2022|
Assessing the reliability of AI for predicting the risk of breast cancer
Improving early diagnosis of major diseases such as cancer and dementia is a top national healthcare priority. Improving screening programmes by making better use of data and digital technologies is a promising way to achieve this goal but there are questions around explainability and trustworthiness of automated screening decisions. A framework to assess the validity of such methods is needed to ensure they are reliable and of clinical use.
This project will develop a framework to assess the reliability of AI for predicting the risk of breast cancer. The OPTIMAM Database managed by the RSNFT contains data for over 150,000 women attending breast screening. The size of the database and the long-term longitudinal nature of this data resource make it ideally placed to aid in predicting risk of breast cancer. This project will use this data resource using AI applied to mammograms and other patient data such as age, breast density and ethnicity and, where possible, imaging data from other modalities such as MRI. The performance of an AI algorithm developed at RSNFT will be evaluated and compared with traditional models. It is critical that AI algorithms used in healthcare are reliable, explainable and not biased to any sub-group of the population. This project will investigate the effect of a variety of factors (e.g. image processing, measurements inferred from the image manufacturer, age and ethnicity) on the performance of the algorithm and to improve the generalisability.
This project will develop methodology to assess the generalisability of AI based tools applied to medical imaging. This requires the development of new knowledge and fundamental research in a way which is well-suited to a PhD project. The project brings together metrological analysis and data science skills with medical imaging, and involves working with specialists from several different disciplines, preparing the candidate for a career in either medical research or data-intensive metrology, and contributing to the growing need for trained experts in AI and big data in healthcare.
The project is a collaboration between the University of, the National Physical Laboratory and the Royal Surrey NHS Foundation Trust. The student will be predominately based at the University of Surrey and RSNFT with possibility of placements at NPL for up to 3 months a year.
UK applicants who hold a First or 2:1 UK honours degree in a relevant subject area, or a 2:2 alongside a good masters degree.
If English is not your first language, you will be required to have an IELTS Academic of 6.5 or above (or equivalent).
How To Apply
Applications can be made through our Physics PhD course page (https://www.surrey.ac.uk/postgraduate/physics-phd)
Please state the project title and supervisor clearly on all applications
1st October 2022
UK Students Only
The studentship is fully funded by EPSRC iCASE voucher number 220022 and NPL top-up of £29,621
Stipend: £15,609/year plus living allowance top up of £2,991/year = Total: £18,600/year
The studentship also covers the tuition fee at the home rate (£4,500/year) and additional funds are available for training, and travel costs for visits to NPL, other project partners and academic conferences.
Closing date for applications: 1 July 2022
For application enquiries, please contact Prof. Rebecca Nutbrown R.Nutbrown@surrey.ac.uk
Studentship ref: PGR-2122-111
Type / Role: