mlsanityData to 14 Oct 2026

Diagnostic Image Quality Assessment and Classification in Medical Imaging: Opportunities and Challenges

arXiv 1912.02907 ↗First posted 5 Dec 2019eess.IV cs.CV cs.LGcited by 6

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Abstract (arXiv)

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Dec 2019.

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Citing papers per year: 2022: 3; 2023: 3.

6 papers.

  1. 2022An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial trainingcited by 3
  2. 2023Estimating Head Motion from MR-Imagescited by 1
  3. 2023Explainable Image Quality Assessment for Medical Imagingcited by 1
  4. 2022Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Imagescited by 0
  5. 2022Shifted Windows Transformers for Medical Image Quality Assessmentcited by 0
  6. 2023Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classificationcited by 0