421 citations · 1.1k across the 23 of their papers we have counts for
23 papers
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
Rui Liu, Yuanyuan Chen, Anran Li +3
Insufficient data is a long-standing challenge for Brain-Computer Interface (BCI) to build a high-performance deep learning model. Though numerous research groups and institutes co…
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction
Ziyuan Zhao, Peisheng Qian, Xulei Yang +4
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing de…
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
Ziyuan Zhao, Fangcheng Zhou, Zeng Zeng +2
Domain shift and label scarcity heavily limit deep learning applications to various medical image analysis tasks. Unsupervised domain adaptation (UDA) techniques have recently achi…
Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5
Su Zhang, Ziyuan Zhao, Cuntai Guan
We used two multimodal models for continuous valence-arousal recognition using visual, audio, and linguistic information. The first model is the same as we used in ABAW2 and ABAW3,…
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo +2
Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentati…
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu +3
While deep learning methods hitherto have achieved considerable success in medical image segmentation, they are still hampered by two limitations: (i) reliance on large-scale well-…
Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition
Mengjiao Hu, Xudong Jiang, Kang Sim +2
Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for…
A Transformer-based deep neural network model for SSVEP classification
Jianbo Chen, Yangsong Zhang, Yudong Pan +2
Steady-state visual evoked potential (SSVEP) is one of the most commonly used control signal in the brain-computer interface (BCI) systems. However, the conventional spatial filter…
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has sho…
OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation
Amrest Chinkamol, Vetit Kanjaras, Phattarapong Sawangjai +5
While there have been increased researches using deep learning techniques for the extraction of vascular structure from the 2D en face OCTA, for such approach, it is known that the…
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
Ziyuan Zhao, Andong Zhu, Zeng Zeng +2
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especial…
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
Ziyuan Zhao, Jinxuan Hu, Zeng Zeng +4
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in cli…
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Ziyuan Zhao, Wenjing Lu, Zeng Zeng +3
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements. Recently, active learning (AL) strategi…
Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3
Su Zhang, Ruyi An, Yi Ding +1
We propose a cross-modal co-attention model for continuous emotion recognition using visual-audio-linguistic information. The model consists of four blocks. The visual, audio, and…
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
Ziyuan Zhao, Kaixin Xu, Shumeng Li +2
The success of deep convolutional neural networks (DCNNs) benefits from high volumes of annotated data. However, annotating medical images is laborious, expensive, and requires hum…
Federated Graph Neural Networks: Overview, Techniques and Challenges
Rui Liu, Pengwei Xing, Zichao Deng +3
With its capability to deal with graph data, which is widely found in practical applications, graph neural networks (GNNs) have attracted significant research attention in recent y…
Deep Optimal Transport for Domain Adaptation on SPD Manifolds
Ce Ju, Cuntai Guan
Recent progress in geometric deep learning has drawn increasing attention from the machine learning community toward domain adaptation on symmetric positive definite (SPD) manifold…
Time-Series Representation Learning via Temporal and Contextual Contrasting
Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen +4
Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representat…
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
Yi Ding, Neethu Robinson, Chengxuan Tong +2
Neuropsychological studies suggest that co-operative activities among different brain functional areas drive high-level cognitive processes. To learn the brain activities within an…
TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition
Yi Ding, Neethu Robinson, Su Zhang +2
The high temporal resolution and the asymmetric spatial activations are essential attributes of electroencephalogram (EEG) underlying emotional processes in the brain. To learn the…
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
Ravikiran Mane, Effie Chew, Karen Chua +5
Lack of adequate training samples and noisy high-dimensional features are key challenges faced by Motor Imagery (MI) decoding algorithms for electroencephalogram (EEG) based Brain-…
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)
Aung Aung Phyo Wai, Yangsong Zhang, Heng Guo +5
Steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI) provides reliable responses leading to high accuracy and information throughput. But achieving high acc…
Machine learning driven synthesis of few-layered WTe2
Manzhang Xu, Bijun Tang, Chao Zhu +14
Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device a…