C3NL

Clinical applications of machine learning in neuroimaging

A Satellite Symposium at BIH 2015

On Sunday 30th August 2015, Dr James Cole from C3NL chaired a symposium at the Brain Informatics & Health conference, hosted by Imperial College London and held at the Royal Geographical Society, in South Kensington, London. For further details please visit the official conference website. The slides from the presentations will be made available on this page, shortly after the conference,

3D mosaic

Symposium organising committee

Co-charing the symposium with Dr Cole are:

  • 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

Programme

Speaker Title Affiliation PDF
Ana Namburete Automated discovery of neurodevelopmental landmarks from 3D ultrasound images of the fetal brain Institute of Biomedical Engineering, University of Oxford
Gareth Ball Combining multimodal image analysis and clinical information to predict neurodevelopmental outcome in preterm infants Centre for the Developing Brain, King’s College London Ball_BIH_2015
Viktor Wottschel Prediction of conversion to Multiple Sclerosis – an information fusion approach using Random Forests Queen Square MS Centre, University College London BIH2015_Wottschel
Kostas Kamnitsas Deep Artificial Neural Networks for Lesion Segmentation in Multi-Sequence Brain MRI Biomedical Image Analysis group, Imperial College London Kamnitsas slides
Cyrus Eierud Mild Traumatic Brain Injury Associations between Radiological Findings and Neurobehavioral Symptoms using Permutation Test and Support Vector Regression National Intrepid Centre of Excellence (NICoE), Bethesda, USA CyrusEierud_NICoE
Romy Lorenz & Rob Leech The Automatic Neuroscientist: towards optimising patient-tailored cognitive rehabilitation therapy Computational, Clinical & Clinical Neuroimaging Laboratory, Imperial College London

Further details

Neuroimaging has given great insights into the biological processes underlying normal cognition and 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 be used to 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 to address this by encouraging 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 and psychiatric disorders. The potential medical applications of machine learning in neuroimaging range from across the lifespan.

Relevant publications

Below are links to some of our published papers using machine learning in clinical neuroimaging