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

arXiv: https://arxiv.org/abs/1912.02907 · HTML: https://mlsanity.com/paper/1912.02907/

## Key facts

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

## Built on by (most cited first, 6 of 6)

- [An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training](https://mlsanity.com/paper/2206.03359/) (2022)
- [Estimating Head Motion from MR-Images](https://mlsanity.com/paper/2302.14490/) (2023)
- [Explainable Image Quality Assessment for Medical Imaging](https://mlsanity.com/paper/2303.14479/) (2023)
- [Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images](https://mlsanity.com/paper/2206.06725/) (2022)
- [Shifted Windows Transformers for Medical Image Quality Assessment](https://mlsanity.com/paper/2208.06034/) (2022)
- [Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification](https://mlsanity.com/paper/2308.06055/) (2023)

## 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.