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ISMRM Fellowships 2017

Congratulations to Danny Alexander and Andrada Ianus on receiving prestigeous ISMRM fellowships. More...

Published: Apr 26, 2017 11:05:24 AM

Prostate work of Yipeng Hu et al on the cover of IEEE Transactions on Biomedical Engineering - April 2017

Congratulations to Yipeng Hu, Dean Barratt et al for their work on prostate biopsies which is on the cover of IEEE Transactions on Biomedical Engineering.

Published: Apr 10, 2017 1:55:35 PM

Echoes Around the Home.

Echoes Around The Home is a cross disciplinary project conceived and led by Dr Nicholas Firth (UCL Computer Science), working with social scientists Prof. Mary Pat Sullivan (Nipissing University Applied and Professional Studies) and Emma Harding (UCL Institute of Neurology), neuropsychologist Prof. Sebastian Crutch (UCL Institute of Neurology) and computer scientist Prof. Daniel Alexander (UCL Computer Science). This project has been funded by a Social Science Plus+ award from The Collaborative Social Science Domain UCL and will begin installing Echo’s in late March. More...

Published: Feb 6, 2017 3:51:52 PM

Janaina Mourao-Miranda

Start: May 10, 2017 01:00 PM
End: May 10, 2017 02:00 PM

Location: UCL Bloomsbury - Roberts 106 Roberts building

Title: Machine learning and neuroimaging in psychiatry


Machine learning techniques have been successfully applied to clinical neuroimaging data leading to a growing body of research focused on diagnosis and prognosis of mental health disorders. However, so far, most of these studies have focused on supervised classification problems, i.e. they summarize the clinical assessment into a single measure (e.g. diagnostic classification) and the output of the models is limited to a probability value and, in most cases, a binary decision (e.g. healthy/patient). Considering that current diagnostic categories in psychiatry fail to align with findings from clinical neuroscience and genetics, this framework fails to capture the underlying biology and fully characterize disease variation. Alternative frameworks, such as unsupervised learning, are therefore needed to study brain diseases whose underlying processes are not yet fully understood and, therefore, might have an unreliable categorical classification. In this talk I will review the machine learning framework commonly applied to neuroimaging in psychiatry and discuss potential alternatives to overcome limitations of this framework.

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