# Expert-Agnostic Ultrasound Image Quality Assessment using Deep Variational Clustering

arXiv: https://arxiv.org/abs/2307.02462 · HTML: https://mlsanity.com/paper/2307.02462/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 40 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Annotation-efficient deep learning for automatic medical image segmentation](https://mlsanity.com/paper/2012.04885/) (2020)
- [A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions](https://mlsanity.com/paper/2206.07579/) (2022)
- [A Tour of Unsupervised Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1812.07715/) (2018)
- [Variational Clustering: Leveraging Variational Autoencoders for Image Clustering](https://mlsanity.com/paper/2005.04613/) (2020)
- [Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey](https://mlsanity.com/paper/2109.02508/) (2021)
- [Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning](https://mlsanity.com/paper/1804.03830/) (2018)
- [Deep Variational Clustering Framework for Self-labeling of Large-scale Medical Images](https://mlsanity.com/paper/2109.10777/) (2021)
- [Unsupervised Task Design to Meta-Train Medical Image Classifiers](https://mlsanity.com/paper/1907.07816/) (2019)

## Built on by (most cited first, 2 of 2)

- [Coaching a Robotic Sonographer: Learning Robotic Ultrasound with Sparse Expert's Feedback](https://mlsanity.com/paper/2409.02337/) (2024)
- [Subspace-Guided Semantic and Topological Invariant Registration for Annotation-Free Ultrasound Plane Quality Control](https://mlsanity.com/paper/2605.25396/) (2026)

## Abstract (arXiv)

Ultrasound imaging is a commonly used modality for several diagnostic and therapeutic procedures. However, the diagnosis by ultrasound relies heavily on the quality of images assessed manually by sonographers, which diminishes the objectivity of the diagnosis and makes it operator-dependent. The supervised learning-based methods for automated quality assessment require manually annotated datasets, which are highly labour-intensive to acquire. These ultrasound images are low in quality and suffer from noisy annotations caused by inter-observer perceptual variations, which hampers learning efficiency. We propose an UnSupervised UltraSound image Quality assessment Network, US2QNet, that eliminates the burden and uncertainty of manual annotations. US2QNet uses the variational autoencoder embedded with the three modules, pre-processing, clustering and post-processing, to jointly enhance, extract, cluster and visualize the quality feature representation of ultrasound images. The pre-processing module uses filtering of images to point the network's attention towards salient quality features, rather than getting distracted by noise. Post-processing is proposed for visualizing the clusters of feature representations in 2D space. We validated the proposed framework for quality assessment of the urinary bladder ultrasound images. The proposed framework achieved 78% accuracy and superior performance to state-of-the-art clustering methods.