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Your search for Keyword: 'Machine Learning' returned 16 Result(s)
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Mr Thomas Biggans
Dr Amir Dehsarvi
Classification, Cartesian Genetic Programming, Evolutionary Algorithms, Machine Learning, MRI/fMRI data analysis, Mediation Analysis, and Neurodegenerative Diseases Diagnosis (Biomarkers) and Monitoring.
Dr Paola Galdi
Machine learning, Big Data Analysis, Data Integration, Neonatal MRI, Cognitive Neuroscience, Predictive Modelling
Dr Blair Johnston
Research for patient benefit: MRI physics, MRI safety, image analysis and clinical application of AI
Kirsty Jones
Mr Philipp Loske
Extraction of morphological networks based on structural MRI, analysing network measures that can help in providing neuroimaging makers for dementia.
Dr Esma Mansouri-Benssassi
Dr Alessandro Perelli
My main research interests are primarily related to computational imaging, and machine learning, in particular for low-dose poly-energetic 3D/4D and spectral X-ray Computed Tomography (CT) reconstruction.
My particular specialism and passionate interests span to randomized linear algebra, stochastic iterative algorithms and variational inference models.
I am dealing with all aspects of the image reconstruction, from the theoretical mathematical modelling, through the algorithm development, to the handling and processing of experimental CT data together with physical modeling such as scattering.
Miss Natasha Sangha
Mr Fraser Sneden
I am a PhD student within the Institute of Cardiovascular and Medical Sciences, based at the Queen Elizabeth University Hospital in Glasgow. I am working on a project entitled "Magnetic Resonance Imaging to Assess Carotid-Brain Interactions in People with Stroke", as part of the XILO-FIST clinical trial.
I am interested in exploring the relationship between carotid artery structure, brain structure and cognition in ischaemic stroke, and develop an automated tool that will estimate vascular parameters of carotid structure including stenosis. Additionally, I aim to investigate any effects allopurinol has on carotid artery structure, including % stenosis and intima-media thickness.