Medical image analysis is the process of extracting meaningful information from medical images for clinical review and medical intervention. To date, there has been considerable effort in applying image processing techniques to assist clinicians with medical diagnosis and intervention. The rapid development of Deep Learning methodologies in medical image analysis has shown the potential of using these technologies to automate the analysis of medical images in the diagnosis of human diseases [1], reducing likelihood of misdiagnosis while saving on time, labour and cost for clinicians. Medical images capturing the reflection of the interior surface of the eye is called retinal fundus imaging. Such images can be used to provide facts and information about visual disorders such as diabetic retinopathy, glaucoma, age-related macular degeneration, etc.
A growing area of medical research is to evaluate the health of the eye in order to provide a greater insight into the overall health of the human body. While retinal fundus images are used in routine clinical practice to diagnose ophthalmic conditions, evidence suggests that the retinal fundus images contain valuable information regarding the systemic health features of patients [2]. Examples of such diagnoses include brain disease (Alzheimer’s) [3], and heart-related conditions, such as cardiovascular, hypertension, and stroke [4].
This PhD proposal aims to develop appropriate deep learning models for use on retinal fundus images for diagnosing both systemic and localised human diseases. These deep learning models will allow for non-invasive, low-cost, and more accessible patient screening during an ophthalmologic examination, with the goal of accessible and earlier disease detection across various patient populations. Images collected by a trained technician, together with the use of an automated deep learning medical image analysis tool, will contribute to improving holistic patient care beyond ophthalmology by allowing patients to be diagnosed with various conditions in a non-invasive manner
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.
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
The University offers the following levels of support:
The following scholarship options are available to applicants worldwide:
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.
Due consideration should be given to financing your studies. Further information on cost of living
[1] Chowdary, Jignesh, Pratheepan Yogarajah, Priyanka Chaurasia, and Velmathi Guruviah. "A Multi-Task Learning Framework for Automated Segmentation and Classification of Breast Tumors From Ultrasound Images." Ultrasonic Imaging 44, no. 1 (2022): 3-12.
[2] Khan, Nergis C., Chandrashan Perera, Eliot R. Dow, Karen M. Chen, Vinit B. Mahajan, Prithvi Mruthyunjaya, Diana V. Do, Theodore Leng, and David Myung. "Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models." Diagnostics 12, no. 7 (2022): 1714.
[3] Cheung, Carol Y., An Ran Ran, Shujun Wang, Victor TT Chan, Kaiser Sham, Saima Hilal, Narayanaswamy Venketasubramanian et al. "A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study." The Lancet Digital Health 4, no. 11 (2022): e806-e815.
[4] Poplin, Ryan, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, and Dale R. Webster. "Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning." Nature Biomedical Engineering 2, no. 3 (2018): 158-164.
Submission deadline
Monday 27 February 2023
04:00PM
Interview Date
18 April 2023
Preferred student start date
18 September 2023
Telephone
Contact by phone
Email
Contact by email