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Found 24 papers, 12 papers with code
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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 located lung cancer lesions.
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.
Low-rank Tensor Assisted K-space Generative Model for Parallel Imaging Reconstruction
Although recent deep learning methods, especially generative models, have shown good performance in fast magnetic resonance imaging, there is still much room for improvement in high-dimensional generation.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Joint Repetition Suppression and Content Moderation of Large Language Models
Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs).
Accurate Airway Tree Segmentation in CT Scans via Anatomy-aware Multi-class Segmentation and Topology-guided Iterative Learning
Intrathoracic airway segmentation in computed tomography (CT) is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmonary disease (COPD), asthma and lung cancer.
Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage
In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset.
Partition-based K-space Synthesis for Multi-contrast Parallel Imaging
After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image.
Implicit Shape Modeling for Anatomical Structure Refinement of Volumetric Medical Images
In this paper, a unified framework is proposed for 3D shape modelling and segmentation refinement based on implicit neural networks.
PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusion
Precise boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention, especially for boundary confusion in clinical practice.
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients.
