no code implementations • 18 Apr 2024 • Shanshan Wang, Ying Hu, Xun Yang, Zhongzhou Zhang, Keyang Wang, Xingyi Zhang
To address these problems, we propose a Concept-driven Personalized Forgetting knowledge tracing model (CPF) which integrates hierarchical relationships between knowledge concepts and incorporates students' personalized cognitive abilities.
no code implementations • 4 Apr 2024 • Ruoyou Wu, Jian Cheng, Cheng Li, Juan Zou, Jing Yang, Wenxin Fan, Shanshan Wang
The first is the latent feature extractor, which primarily extracts latent space feature maps from LR dMRI and anatomical images while learning structural prior information from the anatomical images.
no code implementations • 2 Apr 2024 • Nazik Elsayed, Yousuf Babiker M. Osman, Cheng Li, Jiong Zhang, Shanshan Wang
Cardio-cerebrovascular diseases are the leading causes of mortality worldwide, whose accurate blood vessel segmentation is significant for both scientific research and clinical usage.
no code implementations • 16 Mar 2024 • Jiawei Li, Sitong Li, Shanshan Wang, Yicheng Zeng, Falong Tan, Chuanlong Xie
When trained using KNN on CIFAR10, MLOD-Fisher significantly lowers the false positive rate (FPR) from 24. 09% to 7. 47% on average compared to merely utilizing the features of the last layer.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 5 Feb 2024 • Shanshan Wang, Ruoyou Wu, Sen Jia, Alou Diakite, Cheng Li, Qiegen Liu, Leslie Ying
The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods.
1 code implementation • 5 Feb 2024 • Jiarun Liu, Hao Yang, Hong-Yu Zhou, Yan Xi, Lequan Yu, Yizhou Yu, Yong Liang, Guangming Shi, Shaoting Zhang, Hairong Zheng, Shanshan Wang
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.
1 code implementation • 5 Feb 2024 • Shanshan Wang, Soumya Tripathy, Toni Heittola, Annamaria Mesaros
In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets.
no code implementations • 21 Jan 2024 • Cheng Li, Weijian Huang, Hao Yang, Jiarun Liu, Shanshan Wang
Particularly, raw radiology reports are refined to highlight the key information according to a constructed clinical dictionary and two model-optimized knowledge-enhancement metrics.
no code implementations • 4 Jan 2024 • Hao Yang, Hong-Yu Zhou, Zhihuan Li, Yuanxu Gao, Cheng Li, Weijian Huang, Jiarun Liu, Hairong Zheng, Kang Zhang, Shanshan Wang
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.
no code implementations • 3 Jan 2024 • Alou Diakite, Cheng Li, Lei Xie, Yuanjing Feng, Hua Han, Shanshan Wang
Recent research has shown the potential of deep learning in multi-parametric MRI-based visual pathway (VP) segmentation.
no code implementations • 3 Jan 2024 • Hao Yang, Hong-Yu Zhou, Cheng Li, Weijian Huang, Jiarun Liu, Yong Liang, Shanshan Wang
Multimodal deep learning utilizing imaging and diagnostic reports has made impressive progress in the field of medical imaging diagnostics, demonstrating a particularly strong capability for auxiliary diagnosis in cases where sufficient annotation information is lacking.
no code implementations • 3 Jan 2024 • Wenxin Fan, Jian Cheng, Cheng Li, Xinrui Ma, Jing Yang, Juan Zou, Ruoyou Wu, Qiegen Liu, Shanshan Wang
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI).
no code implementations • 3 Jan 2024 • Hua Han, Cheng Li, Lei Xie, Yuanjing Feng, Alou Diakite, Shanshan Wang
Secondly, we propose a cross-fusion module that further enhances the fusion of information between the two modalities.
no code implementations • 3 Jan 2024 • Jing Yang, Jian Cheng, Cheng Li, Wenxin Fan, Juan Zou, Ruoyou Wu, Shanshan Wang
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in vivo} human brain.
no code implementations • 3 Jan 2024 • Jiarun Liu, Hong-Yu Zhou, Cheng Li, Weijian Huang, Hao Yang, Yong Liang, Shanshan Wang
Existing contrastive language-image pre-training aims to learn a joint representation by matching abundant image-text pairs.
no code implementations • 3 Jan 2024 • Weijian Huang, Cheng Li, Hong-Yu Zhou, Jiarun Liu, Hao Yang, Yong Liang, Guangming Shi, Hairong Zheng, Shanshan Wang
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.
no code implementations • 28 Dec 2023 • Shanshan Wang, Mamadou Diagne, Miroslav Krstić
In this paper we establish the continuity of the mapping from (a total of five) plant PDE functional coefficients to the kernel PDE solutions, prove the existence of an arbitrarily close DeepONet approximation to the kernel PDEs, and establish that the DeepONet-approximated gains guarantee stabilization when replacing the exact backstepping gain kernels.
no code implementations • 18 Dec 2023 • Yousuf Babiker M. Osman, Cheng Li, Weijian Huang, Shanshan Wang
The other 100 (8, 800 image slices) are for left atrium segmentation.
no code implementations • 1 Dec 2023 • Yuxia Huang, Zhonghui Wu, Xiaoling Xu, Minghui Zhang, Shanshan Wang, Qiegen Liu
After that, the two new objects as the whole data to realize the reconstruction of T2-weighted image.
no code implementations • 20 Nov 2023 • Dian Xu, Shanshan Wang, Feng Gao, Wei Li, Jianmin Shen
In the field of statistical physics, machine learning has gained significant popularity and has achieved remarkable results in recent studies on phase transitions. In this paper, we apply Principal Component Analysis (PCA) and Autoencoder(AE) based on Unsupervised learning to study the various configurations of the percolation model in equilibrium phase transition.
no code implementations • 12 Sep 2023 • Weijian Huang, Cheng Li, Hao Yang, Jiarun Liu, Shanshan Wang
Recently, multi-modal vision-language foundation models have gained significant attention in the medical field.
no code implementations • 27 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.
no code implementations • 25 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.
no code implementations • 21 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.
no code implementations • 21 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.
no code implementations • 10 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.
1 code implementation • 26 Apr 2023 • Haiqin Xie, Cheng Wang, Shicheng Li, Yue Zhang, Shanshan Wang
In the realm of urban transportation, metro systems serve as crucial and sustainable means of public transit.
no code implementations • 15 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.
no code implementations • 12 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.
no code implementations • 15 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.
no code implementations • 28 Feb 2023 • Shanshan Wang, Michael Schreckenberg, Thomas Guhr
Traffic systems can operate in different modes.
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.
Ranked #2 on 3D Semantic Segmentation on OpenTrench3D
no code implementations • 24 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.
no code implementations • 16 Nov 2022 • Yousuf Babiker M. Osman, Cheng Li, Weijian Huang, Nazik Elsayed, Zhenzhen Xue, Hairong Zheng, Shanshan Wang
The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain.
no code implementations • 16 Nov 2022 • Cheng Li, Yousuf Babiker M. Osman, Weijian Huang, Zhenzhen Xue, Hua Han, Hairong Zheng, Shanshan Wang
Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic.
no code implementations • 15 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.
no code implementations • 15 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.
1 code implementation • 10 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.
no code implementations • 15 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.
2 code implementations • 15 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.
no code implementations • 8 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.
no code implementations • 2 Jun 2022 • Shanshan Wang, Archontis Politis, Annamaria Mesaros, Tuomas Virtanen
In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content.
1 code implementation • 8 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).
no code implementations • 6 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.
1 code implementation • 5 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.
1 code implementation • 21 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.
no code implementations • 18 Mar 2022 • Weijian Huang, Cheng Li, Wenxin Fan, Yongjin Zhou, Qiegen Liu, Hairong Zheng, Shanshan Wang
Recovering high-quality images from undersampled measurements is critical for accelerated MRI reconstruction.
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
no code implementations • 19 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.
no code implementations • 15 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.
1 code implementation • 3 Feb 2022 • Shanshan Wang, Ruoyou Wu, Cheng Li, Juan Zou, Ziyao Zhang, Qiegen Liu, Yan Xi, Hairong Zheng
However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited.
1 code implementation • 25 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.
1 code implementation • 19 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.
no code implementations • 8 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.
no code implementations • 23 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.
1 code implementation • 9 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.
no code implementations • 26 Sep 2021 • Zijie Chen, Cheng Li, Junjun He, Jin Ye, Diping Song, Shanshan Wang, Lixu Gu, Yu Qiao
An essential step of RT planning is the accurate segmentation of various organs-at-risks (OARs) in HaN CT images.
no code implementations • 26 Sep 2021 • Junjun He, Jin Ye, Cheng Li, Diping Song, Wanli Chen, Shanshan Wang, Lixu Gu, Yu Qiao
Recent studies have witnessed the effectiveness of 3D convolutions on segmenting volumetric medical images.
1 code implementation • 26 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.
1 code implementation • 7 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.
no code implementations • 13 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.
1 code implementation • 29 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.
no code implementations • 22 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.
no code implementations • 28 May 2021 • Shanshan Wang, Toni Heittola, Annamaria Mesaros, Tuomas Virtanen
More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams.
no code implementations • 31 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.
no code implementations • 31 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.
1 code implementation • 30 Mar 2021 • Viet Anh Nguyen, Fan Zhang, Shanshan Wang, Jose Blanchet, Erick Delage, Yinyu Ye
Despite the non-linearity of the objective function in the probability measure, we show that the distributionally robust portfolio allocation with side information problem can be reformulated as a finite-dimensional optimization problem.
no code implementations • 15 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".
1 code implementation • 9 Dec 2020 • Shanshan Wang, Cheng Li, Rongpin Wang, Zaiyi Liu, Meiyun Wang, Hongna Tan, Yaping Wu, Xinfeng Liu, Hui Sun, Rui Yang, Xin Liu, Jie Chen, Huihui Zhou, Ismail Ben Ayed, Hairong Zheng
Automatic medical image segmentation plays a critical role in scientific research and medical care.
no code implementations • 27 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.
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.