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X-WR-RELCALID:efc09d74-9c93-479e-a94f-485231ddccde
X-WR-TIMEZONE:America/Vancouver
X-WR-CALNAME:Modelling and Propagating Uncertainties in Machine Learning fo
 r Medical Images Acquired from Patients with Neurological Diseases - Tal A
 rbel\, Professor\, McGill University
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TZID:America/Vancouver
TZUNTIL:20231105T090000Z
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TZNAME:PST
DTSTART:20201101T020000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
RDATE:20211107T020000
RDATE:20221106T020000
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DTSTART:20210314T020000
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RDATE:20230312T020000
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DTSTAMP:20260122T175123Z
CLASS:PUBLIC
CREATED:20210831T193559Z
DESCRIPTION:This event is co-hosted by the Centre for Artificial Intelligen
 ce Decision-making and Action (CAIDA) and the UBC MS Connect Education Pro
 gram. Please register for this event here. Abstract: Although deep learnin
 g (DL) models have been shown to outperform other frameworks for a variety
  of medical contexts\, inference in the presence of pathology in medical i
 mages presents challenges to popular networks. Errors in deterministic out
 puts lead to distrust by clinicians and hinders the adoption of DL methods
  in the clinic. Moreover\, given that medical image analysis typically req
 uires a sequence of…
DTSTART;TZID=America/Vancouver:20210921T083000
DTEND;TZID=America/Vancouver:20210921T093000
LAST-MODIFIED:20210831T193901Z
LOCATION:Please register to receive the Zoom link
SUMMARY:Modelling and Propagating Uncertainties in Machine Learning for Med
 ical Images Acquired from Patients with Neurological Diseases - Tal Arbel\
 , Professor\, McGill University
TRANSP:OPAQUE
URL:https://caida.ubc.ca/event/modelling-and-propagating-uncertainties-mach
 ine-learning-medical-images-acquired-patients
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