Search Results for author: Shanshan Wang

Found 50 papers, 15 papers with code

Generalizable Learning Reconstruction for Accelerating MR Imaging via Federated Neural Architecture Search

no code implementations27 Aug 2023 Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang

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 Neural Network Fairness +4

Efficient Learned Lossless JPEG Recompression

no code implementations25 Aug 2023 Lina Guo, Yuanyuan Wang, Tongda Xu, Jixiang Luo, Dailan He, Zhenjun Ji, Shanshan Wang, Yang Wang, Hongwei Qin

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.

Image Compression Quantization

Deep Learning of Delay-Compensated Backstepping for Reaction-Diffusion PDEs

no code implementations21 Aug 2023 Shanshan Wang, Mamadou Diagne, Miroslav Krstić

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

no code implementations21 Jul 2023 Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang

Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions.

Federated Learning Image Reconstruction +1

Self-Supervised Federated Learning for Fast MR Imaging

no code implementations10 May 2023 Juan Zou, Cheng Li, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang

SSFedMRI explores the physics-based contrastive reconstruction networks in each client to realize cross-site collaborative training in the absence of fully sampled data.

Federated Learning Image Reconstruction

Model-based Federated Learning for Accurate MR Image Reconstruction from Undersampled k-space Data

no code implementations15 Apr 2023 Ruoyou Wu, Cheng Li, Juan Zou, Qiegen Liu, Hairong Zheng, Shanshan Wang

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.

Federated Learning Image Reconstruction

Few-shot Class-incremental Learning for Cross-domain Disease Classification

no code implementations12 Apr 2023 Hao Yang, Weijian Huang, Jiarun Liu, Cheng Li, Shanshan Wang

The ability to incrementally learn new classes from limited samples is crucial to the development of artificial intelligence systems for real clinical application.

class-incremental learning Cross-Domain Few-Shot +3

MGA: Medical generalist agent through text-guided knowledge transformation

no code implementations15 Mar 2023 Weijian Huang, Hao Yang, Cheng Li, Mingtong Dai, Rui Yang, Shanshan Wang

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.

Clinical Knowledge Inductive Bias

Meta Architecture for Point Cloud Analysis

1 code implementation CVPR 2023 Haojia Lin, Xiawu Zheng, Lijiang Li, Fei Chao, Shanshan Wang, Yan Wang, Yonghong Tian, Rongrong Ji

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.

Iterative Data Refinement for Self-Supervised MR Image Reconstruction

no code implementations24 Nov 2022 Xue Liu, Juan Zou, Xiawu Zheng, Cheng Li, Hairong Zheng, Shanshan Wang

Then, we design an effective self-supervised training data refinement method to reduce this data bias.

Image Reconstruction

DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans

no code implementations15 Nov 2022 Haoran Li, Cheng Li, Weijian Huang, Xiawu Zheng, Yan Xi, Shanshan Wang

In this work, we propose a Deeply supervIsed knowledGE tranSfer neTwork (DIGEST), which achieves accurate brain tumor segmentation under different modality-missing scenarios.

Brain Tumor Segmentation Image Segmentation +2

Adaptive PromptNet For Auxiliary Glioma Diagnosis without Contrast-Enhanced MRI

no code implementations15 Nov 2022 Yeqi Wang, Weijian Huang, Cheng Li, Xiawu Zheng, Yusong Lin, Shanshan Wang

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

1 code implementation10 Nov 2022 Shanshan Wang, Soumya Tripathy, Annamaria Mesaros

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.

Contrastive Learning Self-Supervised Learning

Self-supervised Graph Learning for Long-tailed Cognitive Diagnosis

no code implementations15 Oct 2022 Shanshan Wang, Zhen Zeng, Xun Yang, Xingyi Zhang

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.

Graph Learning

One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction

1 code implementation15 Aug 2022 Hong Peng, Chen Jiang, Jing Cheng, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu

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

no code implementations8 Aug 2022 Juan Zou, Cheng Li, Sen Jia, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang

Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved.

Data Augmentation Image Reconstruction

WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction

1 code implementation8 May 2022 Zongjiang Tu, Die Liu, Xiaoqing Wang, Chen Jiang, Pengwen Zhu, Minghui Zhang, Shanshan Wang, Dong Liang, Qiegen Liu

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

no code implementations6 Apr 2022 Shanshan Wang, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Pengjie Ren

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.

Information Retrieval Retrieval

Multi-Weight Respecification of Scan-specific Learning for Parallel Imaging

1 code implementation5 Apr 2022 Hui Tao, Haifeng Wang, Shanshan Wang, Dong Liang, Xiaoling Xu, Qiegen Liu

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

1 code implementation21 Mar 2022 Zongjiang Tu, Chen Jiang, Yu Guan, Shanshan Wang, Jijun Liu, Qiegen Liu, Dong Liang

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible.

MRI Reconstruction

Robust facial expression recognition with global‑local joint representation learning

no code implementations Multimedia Systems 2022 Chunxiao Fan, zhenxing Wang, Jia Li, Shanshan Wang, Xiao Sun

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

no code implementations19 Feb 2022 Shanshan Wang, Lei Zhang, Pichao Wang

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.

Metric Learning Unsupervised Domain Adaptation

Identifying subdominant collective effects in a large motorway network

no code implementations15 Feb 2022 Shanshan Wang, Michael Schreckenberg, Thomas Guhr

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.

Universal Generative Modeling for Calibration-free Parallel Mr Imaging

1 code implementation25 Jan 2022 Wanqing Zhu, Bing Guan, Shanshan Wang, Minghui Zhang, Qiegen Liu

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

1 code implementation19 Jan 2022 Xianghao Liao, Shanshan Wang, Lanlan Tu, Yuhao Wang, Dong Liang, Qiegen Liu

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

no code implementations8 Jan 2022 Yeqi Wang, Longfei Li, Cheng Li, Yan Xi, Hairong Zheng, Yusong Lin, Shanshan Wang

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.

Lesion Segmentation Representation Learning

Radiomic biomarker extracted from PI-RADS 3 patients support more eìcient and robust prostate cancer diagnosis: a multi-center study

no code implementations23 Dec 2021 Longfei Li, Rui Yang, Xin Chen, Cheng Li, Hairong Zheng, Yusong Lin, Zaiyi Liu, Shanshan Wang

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

1 code implementation9 Dec 2021 Chun-Mei Feng, Yunlu Yan, Shanshan Wang, Yong Xu, Ling Shao, Huazhu Fu

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.

Federated Learning Image Reconstruction +1

Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework

1 code implementation26 Sep 2021 Chen Hu, Cheng Li, Haifeng Wang, Qiegen Liu, Hairong Zheng, Shanshan Wang

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.

Model Optimization MRI Reconstruction +1

MRI Reconstruction Using Deep Energy-Based Model

1 code implementation7 Sep 2021 Yu Guan, Zongjiang Tu, Shanshan Wang, Qiegen Liu, Yuhao Wang, Dong Liang

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.

Image Generation MRI Reconstruction

Blind Image Quality Assessment for MRI with A Deep Three-dimensional content-adaptive Hyper-Network

no code implementations13 Jul 2021 Kehan Qi, Haoran Li, Chuyu Rong, Yu Gong, Cheng Li, Hairong Zheng, Shanshan Wang

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.

Blind Image Quality Assessment

Few-Shot Electronic Health Record Coding through Graph Contrastive Learning

1 code implementation29 Jun 2021 Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Evangelos Kanoulas, Maarten de Rijke

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.

Contrastive Learning Few-Shot Learning

Deep neural network Based Low-latency Speech Separation with Asymmetric analysis-Synthesis Window Pair

no code implementations22 Jun 2021 Shanshan Wang, Gaurav Naithani, Archontis Politis, Tuomas Virtanen

Time-frequency masking or spectrum prediction computed via short symmetric windows are commonly used in low-latency deep neural network (DNN) based source separation.

Clustering Deep Clustering +2

Forecasting open-high-low-close data contained in candlestick chart

no code implementations31 Mar 2021 Huiwen Wang, Wenyang Huang, Shanshan Wang

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.

Vocal Bursts Intensity Prediction

Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA

no code implementations31 Mar 2021 Wenyang Huang, Huiwen Wang, Shanshan Wang

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.

Dimensionality Reduction

Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data

no code implementations15 Dec 2020 Shanshan Wang, Taohui Xiao, Qiegen Liu, Hairong Zheng

Magnetic resonance imaging is a powerful imaging modality that can provide versatile information but it has a bottleneck problem "slow imaging speed".

Image Reconstruction

Multi-task MR Imaging with Iterative Teacher Forcing and Re-weighted Deep Learning

no code implementations27 Nov 2020 Kehan Qi, Yu Gong, Xinfeng Liu, Xin Liu, Hairong Zheng, Shanshan Wang

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

no code implementations MIDL 2019 Haoyun Liang, Yu Gong, Hoel Kervadec, Jing Yuan, Hairong Zheng, Shanshan Wang

A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data.

Cannot find the paper you are looking for? You can Submit a new open access paper.