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Ultrasound Image Classification using ACGAN with Small Training Dataset
B-mode ultrasound imaging is a popular medical imaging technique. Like o...
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Deep Learning in Ultrasound Elastography Imaging
It is known that changes in the mechanical properties of tissues are ass...
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POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
With the rapid development of COVID-19 into a global pandemic, there is ...
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Estimation of preterm birth markers with U-Net segmentation network
Preterm birth is the most common cause of neonatal death. Current diagno...
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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 anoma...
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Ultrasound Liver Fibrosis Diagnosis using Multi-indicator guided Deep Neural Networks
Accurate analysis of the fibrosis stage plays very important roles in fo...
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Ultrasound Video Summarization using Deep Reinforcement Learning
Video is an essential imaging modality for diagnostics, e.g. in ultrasou...
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Deep learning in the ultrasound evaluation of neonatal respiratory status
Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imaging has been largely adopted in sensitive applications, like the diagnosis and follow-up of preterm newborns in neonatal intensive care units. On the other side, state-of-the-art image analysis and pattern recognition approaches have recently proven their ability to fully exploit the rich information contained in these data, making them attractive for the research community. In this work, we present a thorough analysis of recent deep learning networks and training strategies carried out on a vast and challenging multicenter dataset comprising 87 patients with different diseases and gestational ages. These approaches are employed to assess the lung respiratory status from ultrasound images and are evaluated against a reference marker. The conducted analysis sheds some light on this problem by showing the critical points that can mislead the training procedure and proposes some adaptations to the specific data and task. The achieved results sensibly outperform those obtained by a previous work, which is based on textural features, and narrow the gap with the visual score predicted by the human experts.
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