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Published in 2022 at "Medical physics"
DOI: 10.1002/mp.15982
Abstract: PURPOSE Task automation is essential for efficient and consistent image segmentation in radiation oncology. We report on a deep learning architecture, comprised of a U-Net and a variational autoencoder (VAE) for automatic contouring of the…
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Keywords:
source;
net vae;
dataset;
vae ... See more keywords
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Published in 2019 at "World neurosurgery"
DOI: 10.1016/j.wneu.2019.07.166
Abstract: BACKGROUND Venous air embolism (VAE) is a rare but frequently fatal complication during surgery. Several published reports have shown visible bubbling of air at the surgical site as the first clinical indication of VAE-induced cardiovascular…
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Keywords:
surgery;
vae;
air;
air bubbling ... See more keywords
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Published in 2018 at "Journal of Neurosurgical Anesthesiology"
DOI: 10.1097/ana.0000000000000534
Abstract: Background: Venous air embolism (VAE) is a well-described complication of neurosurgical procedures performed in the seated position. Although most often clinically insignificant, VAE may result in hemodynamic or neurological compromise resulting in urgent change to…
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Keywords:
extreme vae;
air embolism;
position;
vae ... See more keywords
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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3161661
Abstract: This paper presents an analysis of the implementation and performance of a deep learning model based on Recurrent layers and Variational Auto Encoder model (VAE) architecture for prediction of future local trajectory and maneuver. The…
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Keywords:
trajectory prediction;
vehicles trajectory;
vae;
trajectory ... See more keywords
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Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3172333
Abstract: Remarkable progress has been made within nonlinear Independent Component Analysis (ICA) and identifiable deep latent variable models. Formally, the latest nonlinear ICA theory enables us to recover the true latent variables up to a linear…
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Keywords:
ivae gan;
vae;
representation learning;
gan ... See more keywords
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Published in 2023 at "IEEE Signal Processing Letters"
DOI: 10.1109/lsp.2023.3245947
Abstract: Most of existing deep learning models for image forgery localization rely on a large number of high-quality labeled samples for training. The training procedures are performed off-line and without adaptation to the image under scrutiny.…
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Keywords:
image forgery;
forgery localization;
vae;
image ... See more keywords
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Published in 2021 at "IEEE transactions on neural networks and learning systems"
DOI: 10.1109/tnnls.2021.3084957
Abstract: Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different network tasks. Temporal networks are an important type of network whose topological structure changes over…
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Keywords:
attention mechanism;
temporal network;
vae;
network embedding ... See more keywords
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Published in 2021 at "Journal of medical imaging and radiation oncology"
DOI: 10.1111/1754-9485.13316
Abstract: INTRODUCTION Venous air embolization (VAE) from IV lines is a risk with IV contrast administration. The incidence of VAE ranges from 7% to 23%. While life-threatening VAE is rare, there is the potential risk of…
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Keywords:
incidence;
risk;
vae;
sae ... See more keywords
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Published in 2021 at "European Journal of Education"
DOI: 10.1111/ejed.12465
Abstract: This article analyses results of reforms and revisions of the system for recognition of prior learning outcomes in France (Validation des acquis de l'experience, VAE), especially from a quantitative perspective. The development of the recognition…
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Keywords:
system;
recognition prior;
vae;
prior learning ... See more keywords
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Published in 2017 at "Complementary Medicine Research"
DOI: 10.1159/000485743
Abstract: Hintergrund: Orale maligne Melanome (OMM) des Hundes zeichnen sich durch schnelles Wachstum, lokale Invasion und hohe Metastasierungsraten aus. Extrakte auf Basis von Viscum album L. (VAE) werden zunehmend in der Krebstherapie sowohl in der Human-…
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Keywords:
von;
die;
behandlung;
vae ... See more keywords
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Published in 2022 at "PLoS Computational Biology"
DOI: 10.1371/journal.pcbi.1009888
Abstract: A variational autoencoder (VAE) is a machine learning algorithm, useful for generating a compressed and interpretable latent space. These representations have been generated from various biomedical data types and can be used to produce realistic-looking…
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Keywords:
vae;
cell morphology;
cell;
latent space ... See more keywords