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Localising breast cancer risk in mammograms


College of Science

Lincoln United Kingdom Artificial Intelligence Biomedical Engineering Computer Vision Machine Learning

About the Project

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 the Laboratory  of Vision Engineering at Lincoln. 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 and Lincoln. 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). Interested candidates should send their CVs (including references) to

Supervisory team:

  • Dr Faraz Janan, University of Lincoln (Director of Studies/main supervisor)
  • Dr Louise Wilkinson, Oxford University Hospitals NHS Trust
  • Professor Xujiong Ye, University of Lincoln
  • Dr Tryphon Lambrou, University of Lincoln

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.

Who is eligible for funding?

Please make sure to check the eligibility criteria before you apply. Normally, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. Although most DTP students must be UK residents, we also have an opportunity for an international/EU student, so please do consider applying even if you do not meet all of the EPSRC eligibility criteria.

Application

To apply, please complete the application form and send it to


Funding Notes

The University of Lincoln has received funding from the Engineering and Physical Sciences research Council to establish a Doctoral Training Partnership (DTP), which will provide skills training to foster the next generation of world-class research leadership in areas of strategic importance to both EPSRC and the University of Lincoln.

References

Janan, Faraz and Brady, Michael (2018) Focal Asymmetry and Laterality of Cancer in the "Selected Abstracts from the 2018 NCRI Cancer Conference of National Cancer Research Institute". British Journal of Cancer, 119 (1). pp. 1-49. ISSN 1532-1827
Janan, Faraz and Brady, Michael (2018) Tracking ‘developing’ Focal Densities in Breast Quadrants. In: NCRI 2018, Nov 4 - Nov 6, Glasgow.

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