Extended abstract deadline for the symposium - now Friday 26th June at 5pm. Instructions on how to submit an abstract are here.
Clinical applications of machine learning in neuroimaging
Do you use machine learning to address clinical questions with neuroimaging data? We are looking for high-quality abstract submissions from researchers in related fields who are interested in presenting their work at a large, inter-disciplinary, international event. For further details on our symposium, please visit www.c3nl.com/events/bih2015/
Our symposium is part of the Brain Informatics & Health conference, hosted by Imperial College London. The conference takes place from 30th August-2nd September and is being held at the Royal Geographical Society, in South Kensington, London. For further details on the conference please visit the official website, to find out how to register and to view the full conference program.
The symposium organising committee are:
Dr James Cole, C3NL, Imperial College London
Dr Ben Glocker, Biomedical Image Analysis Unit, Imperial College London
Dr Pete Hellyer, Centre for Neuroimaging Science, King’s College London
Dr Paul Aljabar, Biomedical Engineering Department, King’s College London
If you’re interested in taking part or have any questions, please get in touch: [log in to unmask]
Further details
Neuroimaging has given great insights into the biological processes underlying normal cognition and those affected by disease, however, the field is yet to achieve significant impact at a clinical level. The inherently predictive framework of machine learning makes these techniques ideal to augment clinical decision-making, particularly as they can make inference at an individual level, a key part of the developing trend towards personalised medicine. Nevertheless, the translation of cutting-edge machine learning techniques into clinical settings has been limited. This symposium aims encourage researchers from both computer science and applied clinical or medical research to work more closely together and develop novel approaches to applying machine learning techniques. This is essential if we are to fully realise the potential of machine learning to translate neuroimaging data into tools that can directly aid clinicians and lead to improvements in the care and quality of life of patients with a range of neurological and psychiatric diseases.
The session will focus on how machine learning-based analysis of neuroimaging data can be applied in clinical settings. This will cover various techniques used to make classifications or predictions that lead to improvements in diagnosis, prognosis or clinical decision-making in neurological, psychiatric and other disorders. The potential medical applications of machine learning in neuroimaging range from across the lifespan. Therefore, conditions may include:
neonatal brain trauma, due to:
prematurity
hypoxia
developmental disorders:
autism
schizophrenia
Down syndrome
neurological conditions such as:
traumatic brain injury
HIV/AIDS
neuro-oncology
neurodegenerative diseases:
Alzheimer’s
Fronto-temporal lobe dementia (FTLD)
Parkinson’s
Huntington’s
James H Cole, PhD
C3NL
Division of Brain Sciences
Department of Medicine
Imperial College London
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