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SUMMARY:Automated Quality Control of Chest X-Ray Images - Eduardo Gonzále
 z Solares
DTSTART:20221102T131500Z
DTEND:20221102T140500Z
UID:TALK192284@talks.cam.ac.uk
CONTACT:107642
DESCRIPTION:Shortcut learning and reliance on confounding features have ha
 rmed the ability of COVID-19 chest X-ray (CXR) artificial intelligence (AI
 ) models to generalise. I describe an automated quality control (Auto-QC) 
 pipeline developed using the largest COVID-19 CXR dataset curated to date.
  The aim is to rapidly clean CXR data by automatically standardising or re
 jecting images\, whilst providing labels to identify con- founding feature
 s\, such as pacemakers and radiographic projection.
LOCATION:The Hoyle Lecture Theatre + Zoom 
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