PhD Studentship: Label-free Learning from Large-Scale Multi-Modal Medical Images and Its Application to Automated Diagnosis of Eye Diseases

Project Title:

Label-free Learning from Large-Scale Multi-Modal Medical Images and Its Application to Automated Diagnosis of Eye Diseases

Project contact: Prof Liangxiu Han

Funding info:

Home fees (2023/24) included plus an annual stipend paid at the UKRI rate (£17,668 for 2022/23). 

Mode of study: Full time

Eligibility:

Open to home and overseas students. Eligible overseas students will need to make up the difference in tuition fees. 

Key dates:        

Closing date: 16 October 2023

Expected start: January 2024

Project summary

Artificial intelligence (AI) (particularly deep learning) has become the fundamental part of computer-aided medical diagnostics to aid clinical decision makings. As the disease has multiple risk factors, modern AI algorithms heavily rely on large well-annotated multimodal datasets. However, curating human labelled data at scale is expensive, daunting, and subject to individual bias.

This project proposes to include multiple modalities and aims to develop a novel SSL framework, capable of integrating the cross-modal information in the learned representation from unlabelled multi-modal images, with initial focus on its application to automated diagnosis of eye diseases.

Aims and objectives

1. Conduct the comprehensive literature review in relation to SSL and computer-aided disease diagnosis
2. Design a set of pretext tasks to learn representations and model parameters from unlabelled multimodal data, which will be transferred to down-stream tasks (e.g. image classification or segmentation)
3. Develop a novel SSL framework enabling learning powerful cross-model information and apply it to eye disease diagnosis using existing datasets from collaborators and publicly available datasets

Specific requirements of the project

Candidates must have a strong motivation for research and excellent programming skills. Expertise of developing computer vision and machine learning algorithms would be desirable, with an interest in image analysis.

Qualifications:

• A high grade undergraduate degree (first class or upper second) in Computer Science
• A MSc level in Computer Science would be desirable for this post

Skills:

• Knowledge of software development and programming
• Good communication and writing skills
• Self-motivated to conduct research activities independently
• Image analysis/machine learning algorithms would be beneficial
• Able to work as part of a joint academia and industry team

How to apply (include weblink)

Interested applicants should contact Prof Liangxiu Han for an informal discussion.

To apply you will need to complete the online application form for a full-time PhD in Computing and digital technology (or download the PGR application form), by clicking the 'Apply' button, above.

You should also complete the PGR thesis proposal (supplementary information) form addressing the project’s aims and objectives, demonstrating how the skills you have maps to the area of research and why you see this area as being of importance and interest. 

If applying online, you will need to upload your statement in the supporting documents section, or email the application form and statement to mailto:PGRAdmissions@mmu.ac.uk.

Closing date: 16 October 2023.

Expected start: January 2024.

Please quote the reference: SciEng-LH-2023-label-free-learning-2

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