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Doctor of Engineering (EngD) - Graph Convolutional Neural Networks for Medical Image Analysis

  • Full or part time
    Prof D Reid
  • Application Deadline
    Tuesday, April 30, 2019
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

All projects co-funded by the CDT must have sufficient depth and scope to support a doctoral thesis. In making this assessment we therefore require all abstracts to address the following points, and will return abstracts for revision if any points are not addressed explicitly: (a) Ensure a strong connection is made explicitly with the scope of the CDT; (b) Convey the key academic and industrial challenges and objectives; (c) Explain how the project will progress the current state of the art, and in what industrial / academic context; (d) Outline how the project is expected to create new knowledge, and in what domain.
Deep Learning-based Artificial Intelligence, in particular Convolutional Neural Networks (CNNs), has seen exponential growth in many pattern recognition challenges since the early 2010s. While CNNs were originally developed to act on data defined on regular cartesian grids, such as images, audio, and text data, they have recently been extended to work on irregularly connected data, such as graphs. This opens the door to a whole new set of potential applications where graphs arise, either naturally (e.g. surface or volumetric meshes representing shapes) or as a useful intermediate representation (e.g. superpixels representing a medical image).
The project will apply graph CNNs to medical image analysis. For example, to identify and outline (segment) a specific structure of interest in a medical image. This is useful in many clinical areas, including cancer (e.g. tumour segmentation to determine treatment effect), neurodegenerative disorders (e.g. parcellation to detect early dementia), cardiovascular diseases (e.g. quantifying coronary artery disease), and musculoskeletal problems.

The EngD is an alternative to a traditional PhD aimed at students wanting a career in industry. Students spend about 75% of their time working directly with a company in addition to receiving advanced-level training from a broad portfolio of technical and business courses. On completion students are awarded the PhD-equivalent EngD.

Essential Criteria
• Strong physics, mathematics and/or computer science background
• Experience in coding algorithms
• Self-motivated, with an eager scientific curiosity
• Strong oral and written communication skills
• Ability to work both individually and as part of a team

Desirable Criteria
• Experience of machine learning, including deep learning
• An interest in medical applications of technology

Working Environment
Canon Medical Research Europe Ltd, based in Edinburgh, designs and develops leading edge software for medical image visualization and analysis used in CT, MRI and other medical scanners used in hospitals worldwide. We are an R&D centre of excellence with strong academic and research links.
While based in the Canon Medical office the student will be part of the Artificial Intelligence Research team with their own workstation and access to Canon Medical software libraries. Working with a team of experts who develop software to commercial quality, they will be expected to be an active member of the team, utilizing team processes and standards, participating in weekly group meetings, presenting their findings to the wider company and attending regular journal club meetings. They will also be encouraged to network and collaborate with team members and other students on placement within Canon Medical.

Flexible Research Working
We aim to offer an inclusive, flexible and balanced working environment, by being an employer that cares for and respects its employees. To this end, Canon Medical’s Flexible Working Policy will be applicable to the successful applicant while based here. The policy outlines process and possible changes to fit working patterns to the student’s circumstances for shorter or longer periods of time.

Funding Notes

This 4-year (including CDT taught-courses) project is funded jointly by the EPSRC CDT in Applied Photonics, run by Heriot-Watt University and the Industrial Sponsor. The annual stipend is £21,053, plus fees paid which includes an enhancement from the Industrial Sponsor. A substantial consumables and equipment budget is provided by a concurrent EPSRC grant. Travel funding for conference presentations is also available.

Project not available to non UK/EU applicants

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