Keywords: quality control, quality assurance, neural networks, web interface
TL;DR: We train a neural network to assess image quality for large multi-center studies.
Abstract: We present an open-source web tool for quality control of distributed imaging studies. To minimize the amount of human time and attention spent reviewing the images, we created a neural network to provide an automatic assessment. This steers reviewers' attention to potentially problematic cases, reducing the likelihood of missing image quality issues. We test our approach using 5-fold cross validation on a set of 5217 magnetic resonance images.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Application: Radiology
Secondary Subject Area: Detection and Diagnosis
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