Search Results for author:
Found 27 papers, 7 papers with code
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field.
Learning Invariant Representation for Unsupervised Image Restoration
Recently, cross domain transfer has been applied for unsupervised image restoration tasks.
Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network
Structure-preserved denoising of 3D magnetic resonance imaging (MRI) images is a critical step in medical image analysis.
Depth Completion using Geometry-Aware Embedding
The ablation study gives more insights into our method that could achieve significant gains with a simple design, while having better generalization capability and stability.
DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.
LEARN++: Recurrent Dual-Domain Reconstruction Network for Compressed Sensing CT
Compressed sensing (CS) computed tomography has been proven to be important for several clinical applications, such as sparse-view computed tomography (CT), digital tomosynthesis and interior tomography.
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.
Effective face landmark localization via single deep network
In this paper, we propose a novel face alignment method using single deep network (SDN) on existing limited training data.
Low-dose CT denoising with convolutional neural network
To reduce the potential radiation risk, low-dose CT has attracted much attention.
Low-Dose CT via Deep Neural Network
In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention.
Medical Physics
Visual Attention Network for Low Dose CT
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance.
Fourth-Order Nonlocal Tensor Decomposition Model for Spectral Computed Tomography
Spectral computed tomography (CT) can reconstruct spectral images from different energy bins using photon counting detectors (PCDs).
CT Reconstruction with PDF: Parameter-Dependent Framework for Multiple Scanning Geometries and Dose Levels
Current mainstream of CT reconstruction methods based on deep learning usually needs to fix the scanning geometry and dose level, which will significantly aggravate the training cost and need more training data for clinical application.
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems
Finally, by integrating the SRL with ASR, an end-to-end multilingual ASR framework is formulated in a supervised manner, which is able to translate the raw wave into text in one model, i. e., wave-to-text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
MANAS: Multi-Scale and Multi-Level Neural Architecture Search for Low-Dose CT Denoising
Recent advance on neural network architecture search (NAS) has proved that the network architecture has a dramatic effect on the model performance, which indicates that current network architectures for LDCT may be sub-optimal.
IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction
Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods.
PointShuffleNet: Learning Non-Euclidean Features with Homotopy Equivalence and Mutual Information
In this paper, we introduce the homotopy equivalence relation (HER) to make the neural networks learn the data distribution from a high-dimension manifold.
One Network to Solve Them All: A Sequential Multi-Task Joint Learning Network Framework for MR Imaging Pipeline
Magnetic resonance imaging (MRI) acquisition, reconstruction, and segmentation are usually processed independently in the conventional practice of MRI workflow.
A convolutional neural network for teeth margin detection on 3-dimensional dental meshes
The accuracy, recall and precision were validated on 145 dental meshes to rate the best network structures, which were finally tested on another 144 dental meshes.
Unsupervised PET Reconstruction from a Bayesian Perspective
Positron emission tomography (PET) reconstruction has become an ill-posed inverse problem due to low-count projection data, and a robust algorithm is urgently required to improve imaging quality.
Unsupervised Denoising of Optical Coherence Tomography Images with Dual_Merged CycleWGAN
Nosie is an important cause of low quality Optical coherence tomography (OCT) image.
Personal Attribute Prediction from Conversations
Personal knowledge bases (PKBs) are critical to many applications, such as Web-based chatbots and personalized recommendation.
Low-resource Personal Attribute Prediction from Conversation
Previous studies often rely on a relative number of resources such as labeled utterances and external data, yet the attribute knowledge embedded in unlabeled utterances is underutilized and their performance of predicting some difficult personal attributes is still unsatisfactory.
Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond
More specifically, we decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation and then disentangle case-specific noise and high-frequency components in the latent space.
Fast and Stable Diffusion Inverse Solver with History Gradient Update
To address this issue, we propose to utilize the history information of the diffusion-based inverse solvers.
Diffusion Posterior Proximal Sampling for Image Restoration
Specifically, we opt for a sample consistent with the measurement identity at each generative step, exploiting the sampling selection as an avenue for output stability and enhancement.
