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Found 41 papers, 5 papers with code
Fast Multi-class Dictionaries Learning with Geometrical Directions in MRI Reconstruction
The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods.
Projected Iterative Soft-thresholding Algorithm for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
It has been shown that, redundant image representations, e. g. tight frames, can significantly improve the image quality.
Hankel Matrix Nuclear Norm Regularized Tensor Completion for $N$-dimensional Exponential Signals
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.
A Guaranteed Convergence Analysis for the Projected Fast Iterative Soft-Thresholding Algorithm in Parallel MRI
The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging.
pISTA-SENSE-ResNet for Parallel MRI Reconstruction
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time.
Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
MQA: Answering the Question via Robotic Manipulation
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given question.
Towards Embodied Scene Description
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment.
Exponential Signal Reconstruction with Deep Hankel Matrix Factorization
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.
LOCx2, a Low-latency, Low-overhead, 2 x 5.12-Gbps Transmitter ASIC for the ATLAS Liquid Argon Calorimeter Trigger Upgrade
In this paper, we present the design and test results of LOCx2, a transmitter ASIC for the ATLAS Liquid Argon Calorimeter trigger upgrade.
Instrumentation and Detectors
Unsupervised Representation Learning by InvariancePropagation
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
Fault-Aware Robust Control via Adversarial Reinforcement Learning
Robots have limited adaptation ability compared to humans and animals in the case of damage.
Unsupervised Representation Learning by Invariance Propagation
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
A Sparse Model-inspired Deep Thresholding Network for Exponential Signal Reconstruction -- Application in Fast Biological Spectroscopy
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
XCloud-pFISTA: A Medical Intelligence Cloud for Accelerated MRI
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.
Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge
In this paper, we propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects.
One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI).
Rich Action-semantic Consistent Knowledge for Early Action Prediction
Next, we build a bi-directional semantic graph for the teacher network and a single-directional semantic graph for the student network to model rich ASCK among partial videos.
Self-supervised 3D Semantic Representation Learning for Vision-and-Language Navigation
In the Vision-and-Language Navigation task, the embodied agent follows linguistic instructions and navigates to a specific goal.
A Paired Phase and Magnitude Reconstruction for Advanced Diffusion-Weighted Imaging
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
Alternating Deep Low-Rank Approach for Exponential Function Reconstruction and Its Biomedical Magnetic Resonance Applications
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
A Dynamics Theory of Implicit Regularization in Deep Low-Rank Matrix Factorization
Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process.
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably.
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding
Soft-thresholding has been widely used in neural networks.
Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal
Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.
CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis
We have shared our cloud platform at MRSHub, providing free access and service for two years.
One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
Heterogeneous Embodied Multi-Agent Collaboration
To study collaboration among heterogeneous agents, we propose the heterogeneous multi-agent tidying-up task, in which multiple heterogeneous agents with different capabilities collaborate with each other to detect misplaced objects and place them in reasonable locations.
CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis
Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation.
Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging
PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data.
Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence
The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format.
NMR Spectra Denoising with Vandermonde Constraints
Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering.
Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
Stimulate the Potential of Robots via Competition
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents.
Multi-Agent Embodied Question Answering in Interactive Environments
We investigate a new AI task --- Multi-Agent Interactive Question Answering --- where several agents explore the scene jointly in interactive environments to answer a question.
