Search Results for author:
Found 9 papers, 3 papers with code
A Multimodal Approach for Fluid Overload Prediction: Integrating Lung Ultrasound and Clinical Data
This study underscores the effectiveness of multimodal learning for fluid overload management in dialysis patients, offering valuable insights for improved clinical outcomes.
DUCPS: Deep Unfolding the Cauchy Proximal Splitting Algorithm for B-Lines Quantification in Lung Ultrasound Images
This paper addresses these issues with a novel unsupervised deep unfolding network structure (DUCPS).
DUBLINE: A Deep Unfolding Network for B-line Detection in Lung Ultrasound Images
In the context of lung ultrasound, the detection of B-lines, which are indicative of interstitial lung disease and pulmonary edema, plays a pivotal role in clinical diagnosis.
A Semi-supervised Learning Approach for B-line Detection in Lung Ultrasound Images
Studies have proved that the number of B-lines in lung ultrasound images has a strong statistical link to the amount of extravascular lung water, which is significant for hemodialysis treatment.
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Our motivation lies in incorporating the tremendous amount of unsupervised user behavior data from the historical search logs as the complementary graph to facilitate relevance modeling.
Current Advances in Computational Lung Ultrasound Imaging: A Review
In the field of biomedical imaging, ultrasonography has become increasingly widespread, and an important auxiliary diagnostic tool with unique advantages, such as being non-ionising and often portable.
TextGNN: Improving Text Encoder via Graph Neural Network in Sponsored Search
Text encoders based on C-DSSM or transformers have demonstrated strong performance in many Natural Language Processing (NLP) tasks.
End-to-End Framework for Efficient Deep Learning Using Metasurfaces Optics
Deep learning using Convolutional Neural Networks (CNNs) has been shown to significantly out-performed many conventional vision algorithms.
Detection of Ship Wakes in SAR Imagery Using Cauchy Regularisation
A Bayesian method, the Moreau-Yoshida unadjusted Langevin algorithm (MYULA), which is computationally efficient and robust is used to estimate the image in the transform domain by minimizing the negative log-posterior distribution.
