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Publication Detail
Bayesian model selection for pathological data.
  • Publication Type:
    Conference
  • Authors:
    Sudre CH, Cardoso MJ, Bouvy WH, Biessels GJ, Barnes J, Ourselin S
  • Publisher:
    Springer Verlag International
  • Pagination:
    323, 330
  • Published proceedings:
    MICCAI 2014
  • Series:
    LNCS 8673
  • Editors:
    Golland P,Hata N,Barillot C,Hornegger J,Howe R
  • Name of conference:
    MICCAI
  • Conference place:
    Boston
  • Conference start date:
    14/09/2014
  • Conference finish date:
    19/09/2014
  • Addresses:
    Translational Imaging Group, Centre for Medical Image Computing, University College London
    London

    Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London
    London

    Brain Center Rudolf Magnus, Department of Neurology, University Medical Center Utrecht
    Utrecht
Abstract
The detection of abnormal intensities in brain images caused by the presence of pathologies is currently under great scrutiny. Selecting appropriate models for pathological data is of critical importance for an unbiased and biologically plausible model fit, which in itself enables a better understanding of the underlying data and biological processes. Besides, it impacts on one's ability to extract pathologically meaningful imaging biomarkers. With this aim in mind, this work proposes a fully unsupervised hierarchical model selection framework for neuroimaging data which permits the stratification of different types of abnormal image atterns without prior knowledge about the subject's pathological status
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Dept of Med Phys & Biomedical Eng
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Dept of Med Phys & Biomedical Eng
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