Rethinking Ultrasound Augmentation: A Physics-Inspired Approach

Tirindelli, Maria Cristina; Eilers, Christine; Simson, Walter; Paschali, Magdalini; Azampour, Mohammad Farid; Navab, Nassir

doi:10.48550/arxiv.2105.02188

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sezer et al [ 14 ] showed success with using optimized Bayesian non-local means (OBNLM) [ 15 ] to reduce speckle noise as a data augmentation technique by creating one other set of denoised data. Tirindelli et al [ 16 ] went the opposite route and looked at adding realistic artifacts and noise-reflecting deformations, reverberations, and signal-to-noise-ratio to linear ultrasound images, but did not show significant improvement in image classification. SpeckleGAN [ 17 ] also looks to add speckle noise artifacts as a data augmentation technique.…”

Section: Introductionmentioning

confidence: 99%

COVID-Net L2C-ULTRA: An Explainable Linear-Convex Ultrasound Augmentation Learning Framework to Improve COVID-19 Assessment and Monitoring

Zeng,

Ebadi,

Florea

et al. 2024

Sensors

View full text Add to dashboard Cite

While no longer a public health emergency of international concern, COVID-19 remains an established and ongoing global health threat. As the global population continues to face significant negative impacts of the pandemic, there has been an increased usage of point-of-care ultrasound (POCUS) imaging as a low-cost, portable, and effective modality of choice in the COVID-19 clinical workflow. A major barrier to the widespread adoption of POCUS in the COVID-19 clinical workflow is the scarcity of expert clinicians who can interpret POCUS examinations, leading to considerable interest in artificial intelligence-driven clinical decision support systems to tackle this challenge. A major challenge to building deep neural networks for COVID-19 screening using POCUS is the heterogeneity in the types of probes used to capture ultrasound images (e.g., convex vs. linear probes), which can lead to very different visual appearances. In this study, we propose an analytic framework for COVID-19 assessment able to consume ultrasound images captured by linear and convex probes. We analyze the impact of leveraging extended linear-convex ultrasound augmentation learning on producing enhanced deep neural networks for COVID-19 assessment, where we conduct data augmentation on convex probe data alongside linear probe data that have been transformed to better resemble convex probe data. The proposed explainable framework, called COVID-Net L2C-ULTRA, employs an efficient deep columnar anti-aliased convolutional neural network designed via a machine-driven design exploration strategy. Our experimental results confirm that the proposed extended linear–convex ultrasound augmentation learning significantly increases performance, with a gain of 3.9% in test accuracy and 3.2% in AUC, 10.9% in recall, and 4.4% in precision. The proposed method also demonstrates a much more effective utilization of linear probe images through a 5.1% performance improvement in recall when such images are added to the training dataset, while all other methods show a decrease in recall when trained on the combined linear–convex dataset. We further verify the validity of the model by assessing what the network considers to be the critical regions of an image with our contribution clinician.

show abstract