Search Results for author:
Found 10 papers, 3 papers with code
Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics
To address this challenge, a sophisticated large language model system named as Xiwu has been developed, allowing you switch between the most advanced foundation models and quickly teach the model domain knowledge.
Fast Sampling generative model for Ultrasound image reconstruction
Image reconstruction from radio-frequency data is pivotal in ultrafast plane wave ultrasound imaging.
Single PW takes a shortcut to compound PW in US imaging
We hypothesize that a single PW can take a shortcut to reach the diffusion trajectory of PWC, removing the need to begin with Gaussian noise.
Score-based Generative Models for Photoacoustic Image Reconstruction with Rotation Consistency Constraints
Photoacoustic tomography (PAT) is a newly emerged imaging modality which enables both high optical contrast and acoustic depth of penetration.
Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstruction
We find that dynamically masking a high proportion of the channels, e. g., 80%, yields nontrivial self-supervisors in both image and signal domains, which decrease the multiplicity of the pseudo solution to efficiently reconstruct the image from fewer PA measurements with minimum error of the image.
A semi-automatic ultrasound image analysis system for the grading diagnosis of COVID-19 pneumonia
This paper proposes a semi-automatic system based on quantitative characterization of the specific image patterns in lung ultrasound (LUS) images, in order to assess the lung conditions of patients with COVID-19 pneumonia, as well as to differentiate between the severe / and no-severe cases.
Deep image prior for undersampling high-speed photoacoustic microscopy
Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound.
Reconstructing undersampled photoacoustic microscopy images using deep learning
One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed.
Towards Generation and Evaluation of Comprehensive Mapping Robot Datasets
This paper presents a fully hardware synchronized mapping robot with support for a hardware synchronized external tracking system, for super-precise timing and localization.
Robotics
Deep Unfolded Robust PCA with Application to Clutter Suppression in Ultrasound
We compare the performance of the suggested deep network on both simulations and in-vivo rat brain scans, with a commonly practiced deep-network architecture and the fast iterative shrinkage algorithm, and show that our architecture exhibits better image quality and contrast.
