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Found 50 papers, 15 papers with code
Enhancing Representation in Radiography-Reports Foundation Model: A Granular Alignment Algorithm Using Masked Contrastive Learning
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field.
Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model.
Efficient Learned Lossless JPEG Recompression
Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components.
Deep Learning of Delay-Compensated Backstepping for Reaction-Diffusion PDEs
Multiple operators arise in the control of PDE systems from distinct PDE classes, such as the system in this paper: a reaction-diffusion plant, which is a parabolic PDE, with input delay, which is a hyperbolic PDE.
FedAutoMRI: Federated Neural Architecture Search for MR Image Reconstruction
Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions.
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.
Optimizing Energy Efficiency in Metro Systems Under Uncertainty Disturbances Using Reinforcement Learning
In the realm of urban transportation, metro systems serve as crucial and sustainable means of public transit.
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.
Few-shot Class-incremental Learning for Cross-domain Disease Classification
The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application.
MGA: Medical generalist agent through text-guided knowledge transformation
To this end, we propose a novel medical generalist agent, MGA, that can address three kinds of common clinical tasks via clinical reports knowledge transformation.
Transitions between quasi-stationary states in traffic systems: Cologne orbital motorways as an example
Traffic systems can operate in different modes.
Meta Architecture for Point Cloud Analysis
However, the lack of a unified framework to interpret those networks makes any systematic comparison, contrast, or analysis challenging, and practically limits healthy development of the field.
Ranked #6 on
Semantic Segmentation
on S3DIS
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.
DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans
In this work, we propose a Deeply supervIsed knowledGE tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation under different modality-missing scenarios.
Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI
Multi-contrast magnetic resonance imaging (MRI)-based automatic auxiliary glioma diagnosis plays an important role in the clinic.
Self-supervised learning of audio representations using angular contrastive loss
To improve the discriminative ability of feature embeddings in SSL, we propose a new loss function called Angular Contrastive Loss (ACL), a linear combination of angular margin and contrastive loss.
Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis
Cognitive diagnosis is a fundamental yet critical research task in the field of intelligent education, which aims to discover the proficiency level of different students on specific knowledge concepts.
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.
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.
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content.
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).
Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding
In this work, we propose a simple yet effective attention guiding mechanism to improve the performance of PLM by encouraging attention towards the established goals.
Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging
Parallel imaging is widely used in magnetic resonance imaging as an acceleration technology.
K-space and Image Domain Collaborative Energy based Model for Parallel MRI Reconstruction
Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.
Rethinking the optimization process for self-supervised model-driven MRI reconstruction
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction.
Robust facial expression recognition with global‑local joint representation learning
In the proposed method, (1) the topological structure information and texture feature of regions of interest (ROIs) are modeled as graphs and processed with graph convolutional network (GCN) to remain the topological features.
Facial Expression Recognition
Facial Expression Recognition (FER)
+1
BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective
In our work, considering the different importance of pair-wise samples for both feature learning and domain alignment, we deduce our BP-Triplet loss for effective UDA from the perspective of Bayesian learning.
Identifying subdominant collective effects in a large motorway network
In a previous study, we focused on the collectivity motion present in the entire traffic network, i. e. the collectivity of the system as a whole.
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.
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.
Variable Augmented Network for Invertible MR Coil Compression
Additionally, its performance is not susceptible to different number of virtual coils.
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.
Specificity-Preserving Federated Learning for MR Image Reconstruction
The core idea is to divide the MR reconstruction model into two parts: a globally shared encoder to obtain a generalized representation at the global level, and a client-specific decoder to preserve the domain-specific properties of each client, which is important for collaborative reconstruction when the clients have unique distribution.
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.
Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation
Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images.
A Novel Hybrid Convolutional Neural Network for Accurate Organ Segmentation in 3D Head and Neck CT Images
An essential step of RT planning is the accurate segmentation of various organs-at-risks (OARs) in HaN CT images.
MRI Reconstruction Using Deep Energy-Based Model
In contrast to other generative models for reconstruction, the proposed method utilizes deep energy-based information as the image prior in reconstruction to improve the quality of image.
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.
Few-Shot Electronic Health Record Coding through Graph Contrastive Learning
We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task.
Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair
Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation.
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions
More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams.
Forecasting open-high-low-close data contained in candlestick chart
Typically, the existence of the inherent constraints in OHLC data poses great challenge to its prediction, e. g., forecasting models may yield unrealistic values if these constraints are ignored.
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA
The (open-high-low-close) OHLC data is the most common data form in the field of finance and the investigate object of various technical analysis.
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.
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.
