Project Description
This project will investigate the use of deep learning applied to imaging genomics to support precision health.
Precision medicine aims to tailor treatment to the individual, rather than assuming everyone will respond like the average patient. The biggest drivers in precision medicine have been developments in genomics. For example, knowing the genomic make-up of a tumour, e.g., lung cancer, allows clinicians to use highly effective targeted treatments against the tumour. However, a biopsy tissue sample is required to sequence the tumour genome, which is invasive and involves some risk. In addition, rapid mutation means tumours are often genetically heterogeneous. This heterogeneity is difficult to capture in a small biopsy sample, which can mislead and result in ineffective treatment.
Imaging genomics (sometimes known as radiogenomics) uses features derived from non-invasive medical images to infer the spatial distribution of the tumour genotype(s). Traditional imaging features have included the shape, greylevel intensity statistics, and texture of the tumour.
Deep learning is an artificial intelligence neural network technique based on multiple layers of neurons. It has had a huge impact on medical image analysis, setting the state-of-the-art performance in many benchmarks and applications, and outperforming human observers in some situations. This project will determine where deep learning can best be applied in the imaging genomic pipeline, to help ensure that every patient gets the right treatment at the treatment.
The student will have a workstation in Canon Medical Research Europe‘s modern open plan office space in Edinburgh, seated with other members of the AI Research Team. The team use hybrid working – with time spent working from the office or remotely from home. Canon Medical Research Europe has a hybrid working scheme offering flexible hours and remote working for part of the week. We are always happy to discuss flexible options with candidates.
CDT Essential Criteria
A Masters level degree (MPhys, MSc) at 2.1 or equivalent.
Desire to work collegiately, be involved in outreach, undertake taught and professional skills study.
Project Essential Criteria
Strong physics, mathematics, computer science or similar background.
Experience implementing and coding algorithms.
Project Desirable Criteria
Experience in machine learning/deep learning.
Experience in image processing/computer vision.
An interest in healthcare/genomics/medical imaging.
The CDT
The CDT in Applied Photonics provides a supportive, collaborative environment which values inclusivity and is committed to creating and sustaining a positive and supportive environment for all our applicants, students, and staff. For further information, please see our ED&I statement https://bit.ly/3gXrcwg. Forming a supportive cohort is an important part of the programme and our students take part in various professional skills workshops, including Responsible Research and Innovation workshops and attend Outreach Training.
