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A Machine-learning framework for automatic reference-free quality assessment in MRI
Magnetic resonance (MR) imaging offers a wide variety of imaging techniq...
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TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
Telehealth is an increasingly critical component of the health care ecos...
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Full-reference image quality assessment-based B-mode ultrasound image similarity measure
During the last decades, the number of new full-reference image quality ...
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Automatic quality assessment for 2D fetal sonographic standard plane based on multi-task learning
The quality control of fetal sonographic (FS) images is essential for th...
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On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra-observer Variability in 2D Echocardiography Quality Assessment
Uncertainty of labels in clinical data resulting from intra-observer var...
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Deep learning in the ultrasound evaluation of neonatal respiratory status
Lung ultrasound imaging is reaching growing interest from the scientific...
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Line Artefact Quantification in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularisation
In this paper, we present a novel method for line artefacts quantificati...
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Image quality assessment for closed-loop computer-assisted lung ultrasound
We describe a novel, two-stage computer assistance system for lung anomaly detection using ultrasound imaging in the intensive care setting to improve operator performance and patient stratification during coronavirus pandemics. The proposed system consists of two deep-learning-based models. A quality assessment module automates predictions of image quality, and a diagnosis assistance module determines the likelihood-of-anomaly in ultrasound images of sufficient quality. Our two-stage strategy uses a novelty detection algorithm to address the lack of control cases available for training a quality assessment classifier. The diagnosis assistance module can then be trained with data that are deemed of sufficient quality, guaranteed by the closed-loop feedback mechanism from the quality assessment module. Integrating the two modules yields accurate, fast, and practical acquisition guidance and diagnostic assistance for patients with suspected respiratory conditions at the point-of-care. Using more than 25,000 ultrasound images from 37 COVID-19-positive patients scanned at two hospitals, plus 12 control cases, this study demonstrates the feasibility of using the proposed machine learning approach. We report an accuracy of 86 insufficient quality images by the quality assessment module. For data of sufficient quality, the mean classification accuracy in detecting COVID-19-positive cases was 95 the training of any networks within the proposed system.
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