About: Fast brain-wide search of highly discriminative regions in medical images: an application to Alzheimer's disease     Goto   Sponge   NotDistinct   Permalink

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Description
  • We propose a fast algorithm for the identification of localised brain regions from medical images that discriminate between two groups of individuals. The method is based on a combination of penalised regression and a data resampling procedure. We apply this approach to both MRI and PET images for the classification of subjects with Alzheimer's disease and mild cognitive impairment. We show that the voxels selected by the algorithm form connected brain regions which are well known to be affected by Alzheimer's disease. A linear statistical classifier trained on the selected voxels achieves cross-validated classification results that are comparable to those obtained by current state-of-the-art methodologies.
  • We propose a fast algorithm for the identification of localised brain regions from medical images that discriminate between two groups of individuals. The method is based on a combination of penalised regression and a data resampling procedure. We apply this approach to both MRI and PET images for the classification of subjects with Alzheimer's disease and mild cognitive impairment. We show that the voxels selected by the algorithm form connected brain regions which are well known to be affected by Alzheimer's disease. A linear statistical classifier trained on the selected voxels achieves cross-validated classification results that are comparable to those obtained by current state-of-the-art methodologies. (en)
Title
  • Fast brain-wide search of highly discriminative regions in medical images: an application to Alzheimer's disease
  • Fast brain-wide search of highly discriminative regions in medical images: an application to Alzheimer's disease (en)
skos:prefLabel
  • Fast brain-wide search of highly discriminative regions in medical images: an application to Alzheimer's disease
  • Fast brain-wide search of highly discriminative regions in medical images: an application to Alzheimer's disease (en)
skos:notation
  • RIV/00216224:14110/11:00056978!RIV12-MZ0-14110___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(NS10347), P(NS9893)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 199351
http://linked.open...ai/riv/idVysledku
  • RIV/00216224:14110/11:00056978
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Feature selection; penalised regression; sparse classification; MRI; PET; Alzheimer's disease (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [5E48555BB0B2]
http://linked.open...v/mistoKonaniAkce
  • King's College London, UK
http://linked.open...i/riv/mistoVydani
  • London, United Kingdom
http://linked.open...i/riv/nazevZdroje
  • Proceedings of Medical Image Understanding and Analysis 2011
http://linked.open...in/vavai/riv/obor
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http://linked.open...vavai/riv/projekt
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http://linked.open...iv/tvurceVysledku
  • Gray, Katherine
  • Janoušová, Eva
  • Montana, Giovanni
  • Rueckert, Daniel
  • Vounou, Maria
  • Wolz, Robin
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • King's College London
https://schema.org/isbn
  • 1-901725-41-3
http://localhost/t...ganizacniJednotka
  • 14110
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