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SUMMARY:Applications of machine learning methods to neuroimaging problems
DTSTART:20160621T100000
DTEND:20160621T110000
DTSTAMP:20260510T164737Z
UID:95a0b807576b211b5bc2adb231798ee84d8d48bf5ae3108fd0c6e2fd
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Christos Davatzikos\, University of Pennsylvania\, Phil
 adelphie\, PA (USA)\nMachine learning methods have been increasingly used 
 in the past decade to distill complex imaging data down to few informative
  indices that allow us to achieve diagnoses and predictions on individuals
 \, rather than performing group analyses. We review our work in this area\
 , starting with past work on the use of support vector machines to derive 
 diagnostic indices of Alzheimer’s Disease\,  schizophrenia\, and brain 
 cancer as well as predictive indices of future cognitive decline and clini
 cal progression. We also review similar work on deriving brain development
  and aging indices\, deviations from which flag abnormal processes.\nWe co
 ntinue by addressing the important problem of disease heterogeneity\, and 
 discuss current work using probabilistic mapping of imaging data from norm
 al populations to those of diseased populations\, aiming to answer the que
 stion “In how many and which ways do patients X differ from healthy cont
 rols?” rather than “in what way do patients X differ from healthy cont
 rols?”.\nWe showcase these methods in studies of aging\, MCI\, Alzheimer
 ’s Disease and schizophrenia.  Finally\, we conclude with unsupervised 
 ways of reducing complex imaging data down to a relatively small and infor
 mative set of indices\, and showcase these methods on functional connectiv
 ity and structural data of brain aging.\nBio: Christos Davatzikos\, Ph.D.\
 nWallace T. Miller Sr.\, Professor of Radiology\,\nSecondary appt with Ele
 ctrical and Systems Engineering\,\nDirector\, Center for Biomedical Image 
 Computing and Analyticshttp://www.cbica.upenn.edu\nDirector\, Section of B
 iomedical Image Analysishttp://www.rad.upenn.edu/rad.sbia\nJoint Affiliati
 ons: Bioengineering  and Applied Math graduate groups\nUniversity of Penn
 sylvania
LOCATION:Campus Biotech 144-165 http://cnp.epfl.ch/files/content/sites/cnp
 /files/Seminars/PlanSallesCampusBiotechH8-01.pdf
STATUS:CONFIRMED
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