Medical Image Quality Assurance using Deep Learning

Dženan Zukić, Anne Haley, Curtis Lisle, James Klo, Kilian M. Pohl, Hans J Johnson, Aashish Chaudhary

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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