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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Related Experiment Video

Updated: Jan 24, 2026

Wideband Optical Detector of Ultrasound for Medical Imaging Applications
08:21

Wideband Optical Detector of Ultrasound for Medical Imaging Applications

Published on: May 11, 2014

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Consistent explainable image quality assessment for medical imaging.

Caner Ozer1,2, Arda Guler3, Aysel Turkvatan Cansever3

  • 1Computer Engineering Department, Istanbul Technical University, Istanbul, 34467 Türkiye.

Health Information Science and Systems
|January 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an explainable medical image quality assessment system using NormGrad. It reliably detects image quality issues in Chest X-Rays and Cardiac MRIs, improving diagnostic accuracy.

Keywords:
ConsistencyForeign object detectionImage quality assessmentInterpretabilityLVOT detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Accurate medical image quality assessment is vital to prevent misdiagnosis.
  • Manual quality assessment is time-consuming and prone to errors in large datasets.
  • Automated methods lack explainability and robust quantification.

Purpose of the Study:

  • To propose an explainable automated system for medical image quality assessment.
  • To validate the system in foreign object detection (Chest X-Rays) and cardiac MRI.
  • To quantify the reliability and explainability of the proposed methodology.

Main Methods:

  • Utilized NormGrad, an algorithm comparing classifier saliency maps with baseline detectors.
  • Introduced the Difference of Means (DoM) metric to evaluate saliency detector consistency.
  • Validated the system on Object-CXR and LVOT Cardiac MRI datasets.

Main Results:

  • NormGrad significantly outperformed other saliency detectors with high Pointing Game scores (0.863 for Object-CXR, 0.778 for LVOT).
  • The system demonstrated high consistency across different models, with low DoM scores (0.001 for Object-CXR, 0.005 for LVOT).
  • Publicly available code and experiments facilitate reproducibility.

Conclusions:

  • The NormGrad-powered system enhances the reliability of automated medical image quality evaluation.
  • The DoM metric provides a novel approach to assess saliency detector consistency.
  • The system shows promise for clinical adoption in medical imaging quality control.