Generative AI and Active Learning for Foundation Models Applied to Automated Segmentation of Multi-modal Images

This proposal focuses on automatically segmenting diverse medical imaging modalities such as MRI, CT, and pathology through the combined use of Foundation models, Active Learning and Generative AI (Artificial Intelligence). Automatic segmentation helps to reduce the workload on domain experts, including radiologists, in vivo specialists, and pathologists. Training reliable models requires enormous amounts of domain experts’ annotations, which are costly and time-consuming.

Foundation models are primarily trained on natural images, and the project will look at using these models for multi-modal medical images by first enhancing them using active learning and generative AI. We hypothesise that foundation models learn underlying global structures, with transfer learning, using domain-specific images, helping to learn local structures. For example, human MRI and CT data are more widely available than rat and mouse data. Developing methods to transfer learning from human data to animal data would optimise animal use and may bridge the gap between preclinical and clinical research.

However, Foundation models are primarily trained on massive datasets, which are not always accessible. We will use a combination of active learning and generative AI to provide domain-specific images to fine-tune the pre-trained foundation models. Active learning will help determine which images need expert labelling, and generative AI will help the expert better enhance the resolution of specific images.

Eligibility

 –      Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s in a relevant science or engineering-related discipline.

–      Ideally, applicants should have research experience evidenced by publications in journals or conferences.

Before you apply

We strongly recommend that you contact the supervisor for this project before you apply.

How to apply

You will need to submit an online application through our website here: https://uom.link/pgr-apply-2425

When you apply, you will be asked to upload the following supporting documents: 

 •  Final Transcript and certificates of all awarded university level qualifications

•  Interim Transcript of any university level qualifications in progress

•  CV

•  You will be asked to supply contact details for two referees on the application form (please make sure that the contact email you provide is an official university/ work email address as we may need to verify the reference)

•  Supporting statement: A one or two page statement outlining your motivation to pursue postgraduate research and why you want to undertake postgraduate research at Manchester, any relevant research or work experience, the key findings of your previous research experience, and techniques and skills you’ve developed. (This is mandatory for all applicants and the application will be put on hold without it.

•  English Language certificate (if applicable). If you require an English qualification to study in the UK, you can apply now and send this in at a later date. 

Your application will not be processed without all of the required documents submitted at the time of application, and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.

If you have any questions about making an application, please contact our admissions team by emailing FSE.doctoralacademy.admissions@manchester.ac.uk.

Equality, diversity and inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. We know that diversity strengthens our research community, leading to enhanced research creativity, productivity and quality, and societal and economic impact.

We actively encourage applicants from diverse career paths and backgrounds and from all sections of the community, regardless of age, disability, ethnicity, gender, gender expression, sexual orientation and transgender status.

We also support applications from those returning from a career break or other roles. We consider offering flexible study arrangements (including part-time: 50%, 60% or 80%, depending on the project/funder).