Search Results for author:
Found 40 papers, 10 papers with code
Pathology-genomic fusion via biologically informed cross-modality graph learning for survival analysis
The PGHG consists of biological knowledge-guided representation learning network and pathology-genome heterogeneous graph.
Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining
However, it is challenging for existing methods to model long-range global information, where convolutional neural networks (CNNs) are constrained by their local receptive fields, and vision transformers (ViTs) suffer from high quadratic complexity of their attention mechanism.
Multi-modal vision-language model for generalizable annotation-free pathological lesions localization and clinical diagnosis
Defining pathologies automatically from medical images aids the understanding of the emergence and progression of diseases, and such an ability is crucial in clinical diagnostics.
Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques
The development of medical vision-language foundation models has attracted significant attention in the field of medicine and healthcare due to their promising prospect in various clinical applications.
Improving Medical Report Generation with Adapter Tuning and Knowledge Enhancement in Vision-Language Foundation Models
Medical report generation demands automatic creation of coherent and precise descriptions for medical images.
Score-based Diffusion Models With Self-supervised Learning For Accelerated 3D Multi-contrast Cardiac Magnetic Resonance Imaging
Long scan time significantly hinders the widespread applications of three-dimensional multi-contrast cardiac magnetic resonance (3D-MC-CMR) imaging.
Model-driven CT reconstruction algorithm for nano-resolution X-ray phase contrast imaging
In conclusion, a novel model-driven nPCT image reconstruction algorithm with high accuracy and robustness is verified for the Lau interferometer based hard X-ray nano-resolution phase contrast imaging.
Self-Supervised Federated Learning for Fast MR Imaging
SSFedMRI explores the physics-based contrastive reconstruction networks in each client to realize cross-site collaborative training in the absence of fully sampled data.
Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data
However, high heterogeneity exists in the data from different centers, and existing federated learning methods tend to use average aggregation methods to combine the client's information, which limits the performance and generalization capability of the trained models.
SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI
To overcome this challenge, we introduce a novel approach called SPIRiT-Diffusion, which is a diffusion model for k-space interpolation inspired by the iterative self-consistent SPIRiT method.
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction.
Iterative Data Refinement for Self-Supervised MR Image Reconstruction
Then, we design an effective self-supervised training data refinement method to reduce this data bias.
Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation
The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic.
High-Frequency Space Diffusion Models for Accelerated MRI
Diffusion models with continuous stochastic differential equations (SDEs) have shown superior performances in image generation.
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved.
Multimodal Brain Disease Classification with Functional Interaction Learning from Single fMRI Volume
The low representation ability of FC leads to poor performance in clinical practice, especially when dealing with multimodal medical data involving multiple types of visual signals and textual records for brain diseases.
Rethinking the optimization process for self-supervised model-driven MRI reconstruction
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction.
PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging
However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited.
Expert Knowledge-guided Geometric Representation Learning for Magnetic Resonance Imaging-based Glioma Grading
Geometric manifolds of hand-crafted features and learned features are constructed to mine the implicit relationship between deep learning and radiomics, and therefore to dig mutual consent and essential representation for the glioma grades.
Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study
Prostate Imaging Reporting and Data System (PI-RADS) based on multi-parametric MRI classi\^ees patients into 5 categories (PI-RADS 1-5) for routine clinical diagnosis guidance.
Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
Specifically, during model optimization, two subsets are constructed by randomly selecting part of k-space data from the undersampled data and then fed into two parallel reconstruction networks to perform information recovery.
Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network
However, the performance of these methods is limited due to the utilization of simple content-non-adaptive network parameters and the waste of the important 3D spatial information of the medical images.
Deep Manifold Learning for Dynamic MR Imaging
The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals.
Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed".
Annotation-efficient deep learning for automatic medical image segmentation
Automatic medical image segmentation plays a critical role in scientific research and medical care.
Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning
Noises, artifacts, and loss of information caused by the magnetic resonance (MR) reconstruction may compromise the final performance of the downstream applications.
A coarse-to-fine framework for unsupervised multi-contrast MR image deformable registration with dual consistency constraint
Multi-contrast magnetic resonance (MR) image registration is useful in the clinic to achieve fast and accurate imaging-based disease diagnosis and treatment planning.
Deep Low-rank Prior in Dynamic MR Imaging
The deep learning methods have achieved attractive performance in dynamic MR cine imaging.
Laplacian pyramid-based complex neural network learning for fast MR imaging
A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data.
An Unsupervised Deep Learning Method for Multi-coil Cine MRI
Although these deep learning methods can improve the reconstruction quality compared with iterative methods without requiring complex parameter selection or lengthy reconstruction time, the following issues still need to be addressed: 1) all these methods are based on big data and require a large amount of fully sampled MRI data, which is always difficult to obtain for cardiac MRI; 2) the effect of coil correlation on reconstruction in deep learning methods for dynamic MR imaging has never been studied.
LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset
In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.
Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation
From the different modalities, one modality that contributes most to the results is selected as the master modality, which supervises the information selection of the other assistant modalities.
CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke
To address these challenges, this paper proposes a Cross-Level fusion and Context Inference Network (CLCI-Net) for the chronic stroke lesion segmentation from T1-weighted MR images.
Model-based Deep Medical Imaging: the roadmap of generalizing iterative reconstruction model using deep learning
Usually, acquiring less data is a direct but important strategy to address these issues.
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
This paper proposes a multi-channel image reconstruction method, named DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional neural network.
CRDN: Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-enhanced Loss Constraint
In this work, we propose cascaded residual dense networks for dynamic MR imaging with edge-enhance loss constraint, dubbed as CRDN.
AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms
In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block).
DIMENSION: Dynamic MR Imaging with Both K-space and Spatial Prior Knowledge Obtained via Multi-Supervised Network Training
Dynamic MR image reconstruction from incomplete k-space data has generated great research interest due to its capability in reducing scan time.
Classifying Mammographic Breast Density by Residual Learning
Mammographic breast density, a parameter used to describe the proportion of breast tissue fibrosis, is widely adopted as an evaluation characteristic of the likelihood of breast cancer incidence.
