Search Results for author:
Found 9 papers, 4 papers with code
Deep learning-driven pulmonary arteries and veins segmentation reveals demography-associated pulmonary vasculature anatomy
Here we propose a High-abundant Pulmonary Artery-vein Segmentation (HiPaS) framework achieving accurate artery-vein segmentation on both non-contrast CT and CTPA across various spatial resolutions.
Efficient Bayesian Uncertainty Estimation for nnU-Net
Different from previous baseline methods such as Monte Carlo Dropout and mean-field Bayesian Neural Networks, our proposed method does not require a variational architecture and keeps the original nnU-Net architecture intact, thereby preserving its excellent performance and ease of use.
Compressed Sensing for Photoacoustic Computed Tomography Using an Untrained Neural Network
Our method can cooperate with other existing regularization, and further improve the quality.
AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data
In this paper, we firstly propose to employ a novel signal processing method to make sparse PA raw data more suitable for the neural network, concurrently speeding up image reconstruction.
Superposed Wave (s-Wave): Accelerating Photoacoustic Simulation
However, it is still in a developing stage, and a lot of experiments have to be performed in a simulation setting.
Medical Physics
A Jointed Feature Fusion Framework for Photoacoustic Reconstruction
The cross-domain features from limited-view position-wise data and the reconstructed image are fused by a backtracked supervision.
Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs
If light passes through the body tissues, focusing only on areas where treatment needs, such as tumors, will revolutionize many biomedical imaging and therapy technologies.
Deep learning for photoacoustic imaging: a survey
In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging.
Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo
Photoacoustic imaging (PAI) is an emerging non-invasive imaging modality combining the advantages of deep ultrasound penetration and high optical contrast.
