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BEGIN:VEVENT
DTSTAMP:20260611T105532Z
DTSTART;VALUE=DATE-TIME:20180215T150000
DTEND;VALUE=DATE-TIME:20180215T160000
SUMMARY:Applications of Bayesian Machine Learning for Medical Image Compu
 ting
TZID:Europe/London
UID:20180215-8a17841b617a57590161898dafec5b5c@warwick.ac.uk
CREATED:20180212T102659Z
DESCRIPTION:Dr Ali Gooya\, Sheffield University Abstract: Bayesian probab
 ilistic models provide a systematic approach to combine data with an exp
 ert prior knowledge when the estimation of some hidden variables from th
 e observation is required. The incorporation of the prior knowledge in t
 he solution often prevents over and underfitting issues that are commonl
 y faced by non-Bayesian methods. Furthermore\, by capturing the causal r
 elationships between different variables\, these models provide an expla
 nation for data generation. In this talk\, I will present various tasks 
 that we have recently accomplished in our research group\, highlighting 
 the Bayesian principles behind each. I will include examples of estimati
 ng a probabilistic density function for shapes from pointsets\, segmenta
 tion of cells in microscopic images using graphical models\, and diagnos
 is of breast cancers in ultrasound images using sparse models. The talk 
 will conclude with some pointers for the future research orientations in
  this area.
LOCATION:CS102
CATEGORIES:
LAST-MODIFIED:20180212T102659Z
ORGANIZER;CN=Sara Kalvala:
END:VEVENT
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