Multimodal and Explainable AI for Medical Image Analysis

Summary

Research Context

This research theme focuses on multimodal and explainable AI for complex classification and medical imaging.

The projects address multimodal brain tumour characterisation through integration of MRI and digital pathology, and explainable classification of congenital heart disease in paediatric echocardiography.

Research will draw on cost-sensitive machine learning, multi-measurement sensing, deep learning, Vision Transformers and explainable AI, while considering limited labelled data, rare conditions, cross-centre variation, reliability and clinical evaluation. Collectively, the theme aims to develop accurate, interpretable and trustworthy AI systems that combine complementary data sources to support robust real-world decisions.

The projects address priorities in Life & Health Sciences and Software/Cyber and align strongly with the Belfast Region City Deal Centre for Digital Healthcare Technology, advancing trustworthy AI-enabled diagnostics while supporting priorities to improve health outcomes and transform health-service delivery.

Multimodal AI for Brain Tumour Characterisation: Integrating MRI and Digital Pathology Using Vision Transformers

Supervisor Names: Prof. Yaxin Bi

A brain tumour is a neurological disease that can be either malignant or non-malignant, which can affect patients in very different ways.

Recent large-scale studies show that malignant brain tumours continue to cause substantial health problems and deaths. Although Magnetic Resonance Imaging (MRI) provides valuable information about the characteristics of tumours, MRI alone may not be sufficient to establish a definitive tumour diagnosis.

Histopathological examination of tumour tissue is often required, providing important modal information about tumour type, grade and biological characteristics. The proposed project aims to develop a multimodal AI approach that integrates MRI and digital pathology data using Vision Transformers (ViTs) to develop predictive models that could improve tumour identification and support clinical diagnosis.

Explainable multi-modal classification of congenital heart disease in paediatric echocardiography

Supervisor Names: Dr. Patrick McAllister

This PhD project aims to improve the detection and classification of congenital heart disease in children using artificial intelligence.

The project will develop and evaluate multimodal deep learning methods that integrate clinical and imaging data while embedding explainable AI throughout the research process.

The research will address challenges such as limited labelled data, rare conditions, differences between healthcare centres, and variation in imaging equipment. Models will be evaluated using external datasets and feedback from clinicians to assess their accuracy, reliability, safety, and potential impact on clinical workflows.

The project will ultimately support the responsible development of trustworthy AI tools for paediatric cardiac care.

Applying to Multiple Projects: Applications for more than one PhD studentship are welcome, however if you apply for more than one PhD project within Computing, your first application on the system will be deemed your first-choice preference and further applications will be ordered based on the sequential time of submission.

If you are successfully shortlisted, you will be interviewed only on your first-choice application and ranked accordingly. Those ranked highest will be offered a PhD studentship.

In the situation where you are ranked highly and your first-choice project is already allocated to someone who was ranked higher than you, you may be offered your 2nd or 3rd choice project depending on the availability of this project.

The School of Computing at Ulster University holds Athena Swan Bronze Award since 2016 and is committed to promote and advance gender equality in Higher Education.  We particularly welcome female applicants, as they are under represented within the school.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Sound understanding of subject area as evidenced by a comprehensive research proposal detailing background, research questions, aims and methodology
• A comprehensive and articulate personal statement demonstrating interest in the subject area

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• International applicants should hold, or expect to hold, a First Class Honours Degree in a relevant subject area
• International applicants should hold, or expect to hold a relevant Masters Degree at a level equivalent to a distinction at Ulster
• Work experience relevant to the proposed project
• Track record of peer reviewed publications

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)
• Vice Chancellor's Research Scholarship (VCRS)

PhD Scholarships are available for this project and will cover:

• Tuition fees.
• A maintenance allowance of approximately £22,000 (exact rate is to be confirmed) per annum for three years, subject to satisfactory academic performance.
• A Research Training Support Grant (RTSG) of approximately £900 per annum.

To be eligible for these scholarships applicants must: be a UK or Irish national; or have pre-settled or settled status; or have indefinite leave to remain or enter.

Applicants should also meet the residency criteria which requires that they have lived in the EEA, Switzerland, the UK or Gibraltar for at least the three years preceding the start date of the research degree programme i.e. September 2027.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year, or part-time equivalent, funded from any source, including self-financing students, are NOT eligible to apply for an award.

Students in full-time employment are not eligible for an award.

Due consideration should be given to financing your studies.

Part-time PhD scholarships may be available at a 0.5 pro rata rate of the full-time scholarship, which would require a six-year registration period.

The University will award a successful candidate one of the following scholarships for the project, depending on the funding available at the time an admissions offer is made:

1. Department for the Economy (DfE) PhD Scholarships
2. Vice Chancellor’s Research Scholarships (VCRS)

Please note that the Terms and Conditions for scholarships are subject to change for 2027/28.