Professor Howard Bowman (University of Kent and University of
Birmingham) is looking to take on a PhD student to work on interpretable
machine learning applied to data acquired from stroke patients
(https://www.ucl.ac.uk/ploras/). This work will be with Professor Cathy
Price (Welcome Centre for Human Neuroimaging, UCL), whose (PLORAS) team
has collected one of the largest data sets of stroke patients (greater
than 1,000), including structural MRI scans, behaviour and demographics.
A key focus of Cathy Price’s work is to predict the recovery trajectory
of stroke patients from their structural MRI scans, particularly
patients with language deficits (i.e. that are aphasic). Progress has
been made on this using traditional and now deep learning methods.
Critical to clinical uptake of machine learning in this area is the
ability to interpret the predictions it provides in a fashion that can
be communicated to clinicians, patients and carers. We seek to appoint a
PhD student to work on this topic, using methods such as neural-symbolic
techniques. The student will be located in the School of Computing at
the University of Kent, but will regularly visit and work closely with
Cathy Price’s team at the Welcome Centre for Human Neuroimaging.
Expertise in machine learning will be provided by Dr Thomas Hope
(Welcome Centre for Human Neuroimaging, UCL) and Dr Marek Grzes (School
of Computing, University of Kent).
The PhD being advertised will be one of a tranche of scholarships
available in the School of Computing at the University of Kent at
Canterbury. These will be competitively allocated across a range of
topics. To be eligible for these scholarships, please contact Howard
Bowman (h.bow...@kent.ac.uk) before 31st March 2019.
Relevant articles:
Besold, T. R., Garcez, A. D. A., Bader, S., Bowman, H., Domingos, P.,
Hitzler, P., ... & de Penning, L. (2017). Neural-symbolic learning and
reasoning: A survey and interpretation. arXiv preprint arXiv:1711.03902.
Hope, T. M., Seghier, M. L., Leff, A. P., & Price, C. J. (2013).
Predicting outcome and recovery after stroke with lesions extracted from
MRI images. NeuroImage: clinical, 2, 424-433.
Seghier, M. L., Patel, E., Prejawa, S., Ramsden, S., Selmer, A., Lim,
L., ... & Price, C.J. (2016). The PLORAS database: a data repository for
predicting language outcome and recovery after stroke. Neuroimage, 124,
1208-1212.
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