Search Results for author:
Found 15 papers, 7 papers with code
Generative Modeling in Sinogram Domain for Sparse-view CT Reconstruction
When the number of projection view changes, the DL network should be retrained with updated sparse-view/full-view CT image pairs.
Differentiable Topology-Preserved Distance Transform for Pulmonary Airway Segmentation
Detailed pulmonary airway segmentation is a clinically important task for endobronchial intervention and treatment of peripheral lung cancer lesions.
One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction
At the prior learning stage, we first construct a large Hankel matrix from k-space data, then extract multiple structured k-space patches from the large Hankel matrix to capture the internal distribution among different patches.
Re-thinking and Re-labeling LIDC-IDRI for Robust Pulmonary Cancer Prediction
The LIDC-IDRI database is the most popular benchmark for lung cancer prediction.
WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction
Deep learning based parallel imaging (PI) has made great progresses in recent years to accelerate magnetic resonance imaging (MRI).
Faithful learning with sure data for lung nodule diagnosis
In this study, we construct a sure dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure data knowledge through nodule segmentation and malignancy score regression.
LTSP: Long-Term Slice Propagation for Accurate Airway Segmentation
Methods: In this paper, a long-term slice propagation (LTSP) method is proposed for accurate airway segmentation from pathological CT scans.
BREAK: Bronchi Reconstruction by gEodesic transformation And sKeleton embedding
Since the volume of the peripheral bronchi may be much smaller than the large branches in an input patch, the common segmentation loss is not sensitive to the breakages among the distal branches.
Universal Generative Modeling for Calibration-free Parallel Mr Imaging
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions.
Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning
Two main components are incorporated into the network design, namely variable augmentation technology and sum of squares (SOS) objective function.
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation
Compared to other state-of-the-art transfer learning methods, our method accurately segmented more bronchi in the noisy CT scans.
High-dimensional Assisted Generative Model for Color Image Restoration
This work presents an unsupervised deep learning scheme that exploiting high-dimensional assisted score-based generative model for color image restoration tasks.
Joint Intensity-Gradient Guided Generative Modeling for Colorization
Furthermore, the joint intensity-gradient constraint in data-fidelity term is proposed to limit the degree of freedom within generative model at the iterative colorization stage, and it is conducive to edge-preserving.
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure loss under high acceleration factors, we have proposed an iterative feature refinement model (IFR-CS), equipped with fixed transforms, to restore the meaningful structures and details.
Denoising Auto-encoding Priors in Undecimated Wavelet Domain for MR Image Reconstruction
At the stage of prior learning, transformed feature images obtained by undecimated wavelet transform are stacked as an input of denoising autoencoder network (DAE).
