Loughborough University
About the Project
The aim of the study is to develop a model that could analyse Mammography Image while incorporating the statistical structure and protect patient privacy. Radiology is pivotal in diagnosing various medical conditions, yet interpreting medical images is complex and demands specialised expertise. Recent surges in AI have rapidly advanced medical imaging, offering new possibilities for improving accuracy, efficiency and patient outcomes. Large Language Models(LLMs) have had huge success in linguistic tasks, but it hasn’t been tested in radiology, which is much more complicated and specialised due to the internal structure of medical images. Additionally, LLMs bring privacy issues and produce unfair results if there exists bias in the data.
A high-quality, well-maintained database is a paramount factor in ensuring Artificial Intelligence (AI)’s fairness and unbiasedness. We will study the OPTIMAM Mammography Image Database (OMI-DB), which is a centralized, fully annotated dataset. Whilst existing approaches are constrained by small(around 1000), single-centre datasets, our research stands out by concurrently addressing the statistical information of OPTIMAM and privacy protection. Additionally, its versatility extends to other diseases, enabling enhanced preparedness for future similar requirements.
94% of Loughborough’s research impact is rated world-leading or internationally excellent. REF 2021
Supervisors
- Primary supervisor: Dr Peng Liu
Entry requirements
Applicants should have, or are expected to achieve, at least a 2:1 Honours degree (or equivalent) in Mathematics or in a related subject.
English language requirements
Applicants must meet the minimum English language requirements. Further details are available on the International website.
Start date
October 2025
Tuition fees for 2025-26 entry
- UK fee – To be confirmed Full-time degree per annum
- International fee – £22,360 Full-time degree per annum
Fees for the 2025-26 academic year apply to projects starting in October 2025.
How to apply
All applications should be made online. Under programme name, select; Mathematical Sciences. Please quote the advertised reference number: MA/PL-SF3/2025 in your application.
To avoid delays in processing your application, please ensure that you submit a CV and the minimum supporting documents.
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Please note, applications for this project are considered on an ongoing basis once submitted and the project may be withdrawn prior to the application deadline, if a suitable candidate is chosen for the project.
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