Medical Image Quality Assurance using Deep LearningDownload PDF

22 Apr 2022, 20:50 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • 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.
  • Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Application: Radiology
  • Secondary Subject Area: Detection and Diagnosis
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
1 Reply