Explainable Image Quality Analysis of Chest X-RaysDownload PDF

Feb 09, 2021 (edited Apr 25, 2021)MIDL 2021 Conference SubmissionReaders: Everyone
  • Keywords: Saliency detection, Image Quality Analysis, X-Ray, Foreign Object Detection, NormGrad
  • TL;DR: We explain the image quality problems within Chest X-Rays by using NormGrad.
  • Abstract: Medical image quality assessment is an important aspect of image acquisition where poor-quality images may lead to misdiagnosis. In addition, manual labelling of image quality after the acquisition is often tedious and can lead to some misleading results. Despite much research on the automated analysis of image quality for tackling this problem, relatively little work has been done for the explanation of the methodologies. In this work, we propose an explainable image quality assessment system and validate our idea on foreign objects in a Chest X-Ray (Object-CXR) dataset. Our explainable pipeline relies on NormGrad, an algorithm, which can efficiently localize the image quality issues with saliency maps of the classifier. We compare our method with a range of saliency detection methods and illustrate the superior performance of NormGrad by obtaining a Pointing Game accuracy of 0.862 on the test dataset of the Object-CXR dataset. We also verify our findings through a qualitative analysis by visualizing attention maps for foreign objects on X-Ray images.
  • Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
  • Source Code Url: https://github.com/canerozer/explainable-iqa
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
  • Data Set Url: Object-CXR: https://academictorrents.com/details/fdc91f11d7010f7259a05403fc9d00079a09f5d5
  • Paper Type: validation/application paper
  • Source Latex: zip
  • Primary Subject Area: Application: Radiology
  • Secondary Subject Area: Interpretability and Explainable AI
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