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Automated Quality Control of Chest X-Ray Images

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Shortcut learning and reliance on confounding features have harmed 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 rejecting images, whilst providing labels to identify con founding features, such as pacemakers and radiographic projection.

This talk is part of the Institute of Astronomy Seminars series.

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