This
PhD research would focus 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 (Wellcome 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. The PhD student would 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 Wellcome
Centre for Human Neuroimaging. Expertise in machine learning will be provided
by Dr Thomas Hope (Wellcome Centre for Human
Neuroimaging, UCL) and Dr Marek Grzes (School of
Computing, University of Kent).
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.