RiiSE20 Conference [postponed] Apr 04, 2020 08:30 AM - 05:00 PM — Chancellor’s Building, Edinburgh BioQuarter
Scottish Clinical Imaging Network (SCIN) Annual Event 2020 [postponed] Apr 30, 2020 09:00 AM - 04:00 PM — Glasgow Caledonian University
NCITA National Conference: Translating Imaging Biomarkers for Improved Patient Outcomes [postponed] May 05, 2020 10:00 AM - 05:00 PM — New Hunt's House, Guy's Campus, King's College London
Scottish Radiological Society Spring Meeting 2020 [postponed] May 15, 2020 09:00 AM - 04:10 PM — Centre for Health Science, Inverness
2020 SINAPSE ASM Jun 19, 2020 09:00 AM - 05:00 PM — Virtual Meeting (online)

eLearning

SINAPSE experts from around Scotland have developed ten online modules designed to explain medical imaging. They are freely available and are intended for non-specialists.


Edinburgh Imaging Academy at the University of Edinburgh offers the following online programmes through a virtual learning environment:

Neuroimaging for Research MSc/Dip/Cert

Imaging MSc/Dip/Cert

PET-MR Principles & Applications Cert

Applied Medical Image Analysis Cert

Online Short Courses

People

Your search for Keyword: 'Predictive Classification' returned 11 Result(s)

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Mrs Jyothsna Divyananda


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Dr Javier Escudero

In my research, I create and apply data analysis tools to extract information from biomedical signals and clinical time series.

 

My main aim is to reveal the subtle changes that major diseases (e.g., Alzheimer's and epilepsy) cause in the brain activity.

 

In collaboration with researchers at Edinburgh, across the UK and overseas, I am currently working in the processing and analysis of biomedical signals, particularly human brain activity. By developing and applying signal processing methods, I aim at increasing our understanding of how several brain conditions progress. Of particular interest is the evaluation of brain functional connectivity in both neurodevelopmental and neurodegenerative diseases to understand how they affect the way in which different brain regions interact with each other. I am also interested in the interplay between structure and function in the brain and in the application of pattern recognition techniques to highly-dimensional clinical datasets to support decision making. Finally, I also work in the development of non-invasive methods for rehabilitation purposes, being either the dexterous controls prostheses for amputees or brain-computer interfaces.

 

For additional information, please see: http://www.research.ed.ac.uk/portal/jescuder

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Dr Paola Galdi


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Miss Maria Goni


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Dr Jano van Hemert

Retinal imaging, image processing, automated image analysis, machine learning, medical devices, ophthalmology, eye health care, eye diseases, systemic diseases apparent in the eye.

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Dr Robin Ince


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Mr Blair Johnston

Description of PhD:

 

Diagnostic categorisation of scans from individual subjects with ‘functional’ psychiatric disorders (e.g. depression, schizophrenia, ADHD, addictions) is not possible in radiology.  Quantitative neuroimaging inferences (e.g. using SPM or FSL) have only been possible at a group level, not an individual level.  Recently though, machine learning techniques, such as support vector machines (SVMs), have been applied to individual scan categorisation.1,2,3Typically about 80% of a neuroimaging data set  is used to train a SVM classifier and the remainder is used to test the accuracy.  Considerable success has been reported: e.g.  ~85 to 95% accuracy in schizophrenia1 and unipolar major depression.Attention Deficit Hyperactivity Disorder (ADHD) is the most commonly diagnosed psychiatric disorder in children affecting 3 to 5%.  Whilst ~70% of children will respond to and tolerate methylphenidate (MPH), 30% do not.  There are  currently no ways to identify responders from non-responders without exposing the child/young person to a trial of the drug.  Developing methods to identify stimulant non-responders at an individual level and prior to exposure is a clinically important under-researched area. Neuroimaging studies have identified group level quantitative differences in brain structure, distinguishing treatment responders from non-responders. and positive from negative developmental outcomes.  The successful candidate would focus on further developing the high accuracy, hybrid VBM-FBM-SVM image processing technique, started at Dundee University, applying it to ADHD MPH response prediction.  Given the accuracy of categorisation that has been achieved, further development of machine learning based image categorisation techniques has the potential to make a radical advance in neuroimaging, in general, by the introduction of quantitative methods that can inform clinical decisions on individual patients, with ‘functional’ psychiatric, and other disorders.

 

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Dr Rajeev Krishnadas

I completed my basic medical training from Govt Medical College, Thrissur, India, and MD in psychiatry from TN Medical college, Bombay University, India. Following this, I was sponsored by the British Council PGME scheme to train in psychiatry in the UK.  I am a member of the Royal College of Psychiatrists, and completed my PhD from the University of Glasgow.

I currently work as a Consultant Psychiatrist with the NHS GGC in ESTEEM, the only early intervention in psychosis team in Scotland.

Academic area of interest include using functional MRI and functional connectivity to predict outcomes in complex mental health problems. Linking inflammation to brain connectivity.

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Dr Kristin Nicodemus

Social cognition, neuroeconomics, computational psychiatry, schizophrenia, anxiety, bipolar disorder, depression, imaging genetics

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Miss Emma Pead

Assessing neurodegeneration of the retina and brain with ultra-widefield retinal images.

Philosophers have often described the eye as the window to the soul. This can also be addressed on the scientific basis that the retina is a direct window into the health of the brain (or CNS). My focus is on the development of automatic detection algorithms of biomarkers for Age-related macular degeneration (AMD) and Alzheimer’s disease (AD) in retinal images. AMD is often characterised by the presence of drusen, small deposits of cellular debris, in retinal images appearing as bright white/yellow spots. The hallmark of AD is the presence of Aβ-amyloid which has also been shown to be present in drusen. There are currently no treatments for late AMD, therefore early diagnosis and detection of drusen could provide timely treatment. Additionally, the potential of drusen as an early biomarker of AD will be explored. My methods focus on image analysis and quantification of fundus and OptoMap images (Optos) and application of machine learning algorithms as an automatic detection strategy.

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Professor Douglas Steele

Psychiatry, neurology, brain imaging: http://dslink333.dyndns.org/

 

 

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