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


   Department of Computer Science

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  Dr Mauricio Alvarez, Dr Fariba Yousefi  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

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 supervisors for this project before you apply. Please note that Dr Fariba Yousefi is based at AstraZeneca.

How to apply

To be considered for this project you’ll need to complete a formal application through our online application portal.

When applying, you’ll need to specify the full name of this project, the name of your supervisor, how you’re planning on funding your research, details of your previous study, and names and contact details of two referees.

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 [Email Address Removed].

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).

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Biological Sciences (4) Computer Science (8) Mathematics (25)

Funding Notes

The 3.5-year PhD degree starts in October 2024. The project is fully funded by the Doctoral Training Program plus AstraZeneca funding. Tuition fees will be paid and you will receive a tax free stipend of at least £18,622 per annum.

References



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