Explainable AI for Enhanced Medical Image Analysis

The field of medical image analysis has witnessed considerable advancements in recent years, driven by the availability of large datasets and deep learning techniques. These advancements have opened up new possibilities for the early detection, diagnosis, and treatment of various medical conditions. Yet, the successful integration of artificial intelligence (AI) in healthcare is not without its challenges and complexities, particularly with ensuring the trust and confidence of medical professionals and patients.

AI algorithms, while exceptionally proficient in processing and interpreting vast quantities of medical image data, often operate as black-box models, concealing the rationale behind their decisions and predictions. This lack of transparency raises concerns about their reliability, safety, and ethics, which are of paramount importance in the healthcare sector. Moreover, healthcare professionals and patients must be able to comprehend and trust the AI systems that are aiding in medical decision-making. This necessitates the development of Explainable AI (XAI) techniques specifically tailored to medical image analysis.

The primary objective of this research is to design and develop an XAI framework that enhances the interpretability of AI models used in medical image analysis. The specific goals include:

Please note that a research proposal is NOT required for this project.

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.

• Experience using research methods or other approaches relevant to the subject domain
• A comprehensive and articulate personal statement
• A demonstrable interest in the research area associated with the studentship

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

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Experience using research methods or other approaches relevant to the subject domain
• Work experience relevant to the proposed project
• Publications - peer-reviewed
• Experience of presentation of research findings

The University offers the following levels of support:

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

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) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• 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) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., ... & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

Van der Velden, Bas HM, et al. "Explainable artificial intelligence (XAI) in deep learning-based medical image analysis." Medical Image Analysis 79 (2022): 102470.

Chaddad, Ahmad, et al. "Survey of explainable AI techniques in healthcare." Sensors 23.2 (2023): 634.

Bourdon, Pascal, et al. "Explainable ai for medical imaging: Knowledge matters." Multi-faceted Deep Learning: Models and Data (2021): 267-292.