Published on: 12th May 2017
This project is part of our investigation into novel biomarkers for risk assessment of neurodegenerative conditions, mainly dementia, from a rich set of patient data including full genetic profiles. The student will apply statistics and data analytics techniques (machine learning, deep learning), and learn to use state-of-the-art image measuring packages (e.g. VAMPIRE for retinal images) . It leverages large, cross-linked bioresources (eg goDARTS) which our team have accessed in previous projects. This project builds upon a 1.1M EPSRC grant (with Edinburgh) on multi-modal retinal biomarkers for vascular dementia (2015-8) and a Leverhulme project (2012-5) on retinal measurements and genetics, and complements starting projects like the 5M NIHR Global Health programme grant on diabetes-related data analytics. It is a strong strategic fit with our University strategy on health and biomedical informatics.
(This PhD Studentship is funded by the MRC)
Alex Doney & Emanuele Trucco.
If you would like to discuss the project then please e-mail: email@example.com
R Annunziata, E Trucco: Accelerating Convolutional Sparse Coding for Curvilinear Structures Segmentation by Refining SCIRD-TS Filter Banks. IEEE Transactions on Medical Imaging, vol 35 no 11, Nov 2016, pp 2381-2392.
Yann LeCun, Yoshua Bengio & Geoffrey Hinton: Deep learning. Nature 521, 436–444 (28 May 2015) doi:10.1038/nature14539
S. McGrory, A. M. Taylor, J. Corley, A. Pattie, S. R. Cox, B. Dhillon, J. M. Wardlaw, F. N. Doubal, J. M. Starr, E. Trucco, T. J. MacGillivray, I. J. Deary, Retinal microvascular network geometry and cognitive abilities in community-dwelling older people: The Lothian Birth Cohort 1936 Study. British Journal of Ophthalmology, bjophthalmol-2016-309017; DOI: 10.1136/bjophthalmol-2016-309017