Localising breast cancer risk in mammograms

The project aims to develop a breast cancer diagnosis support system by localizing the future risk of developing cancer, in particular associated with mammographic focal densities. By analysing time series data with new mammographic image analysis framework, the project aims to propose a CAD system that can flag women likely to develop breast cancer in near future. The system should also be able to suggest among the bilateral breast, as well as the location/quadrant where the risk of developing cancer is higher.

Applications are invited for a PhD studentship to work on a project using a range of advanced AI and image analysis techniques to study mammographic images in the context of breast cancer risk. The project will require a full-time research commitment and will be based in School of Computing and Information Science at the Anglia Ruskin University Cambridge. It will combine mammographic density quantification with an AI based classification and pattern recognition framework (desirably Deep Learning methods). It would evaluate the methods developed on mammograms acquired from the Optimam that have negative priors, CC and MLO views available for both breasts and depict biopsy-proven cancers, as well as normal cases. This would help us to assess the effectiveness of a CAD system and its suitability in a clinical set up.

The 3-year project will be carried out in close collaboration with scientists and breast radiologists at Oxford. The successful candidate will be required to apply, develop and program algorithms in the area of computer vision and machine learning, while applied to mammographic images -including but not limited to x-ray and Digital Breast Tomosynthesis (DBT).

Skills the candidate will learn:

Mammographic image analysis, clinical aspects of breast cancer detection and diagnostics, deep learning methods applied to medical imaging.

Ideal candidates:

A strong academic track record with a 2:1 or higher degree in computer science, mathematics, biomedical engineering, electrical(computer) engineering’s or its equivalent if outside the UK. The desirable candidate should have an excellent performance in a relevant postgraduate degree. The candidate is expected to demonstrate expertise of coding in Matlab and python, with good knowledge of image processing techniques. A prior working experience of deep learning methods is desirable. The candidate should be willing to work in close collaboration with clinical radiologists.

If you would like to discuss this research project prior to application please contact [Email Address Removed]

Candidate requirements

Applications are invited from UK Home fee status only. Applicants should have (or expect to achieve) a minimum upper second class undergraduate degree (or equivalent) in a cognate discipline. A Master’s degree in a relevant subject is desirable.

Applicants must be prepared to study on a full-time basis, attending at our Cambridge campus.

Application Procedures

Applications for a Vice Chancellor’s PhD Scholarship are made through the application portal on our website: https://aru.ac.uk/research/postgraduate-research/vc-phd-scholarships

We will review all applications after the submission deadline of 19^th March. We will contact shortlisted applicants in the week commencing 3^rd April. Interviews will be held between 17^th April to 2nd May.

If you have any queries relating to the application process or the terms and conditions of the Scholarships, please email [Email Address Removed].

Documentation required

You will need the following documents available electronically to upload them to the application portal (we can accept files in pdf, jpeg or Word format):

• Certificates and transcripts from your Bachelor and Masters degrees, (if applicable)
• Your personal statement explaining your suitability for the project
• Passport and visa, or evidence of EU Settlement Scheme (if applicable)
• Curriculum Vitae

Please note the application form will ask you to upload a research proposal. You should upload your personal statement in this section, as proposals are not required for this scholarship.