Search Results for author: Cuntai Guan

Found 42 papers, 30 papers with code

Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning

no code implementations14 Aug 2023 Rui Liu, YuanYuan Chen, Anran Li, Yi Ding, Han Yu, Cuntai Guan

Though numerous research groups and institutes collect a multitude of EEG datasets for the same BCI task, sharing EEG data from multiple sites is still challenging due to the heterogeneity of devices.

EEG Eeg Decoding +1

SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction

no code implementations15 May 2023 Ziyuan Zhao, Peisheng Qian, Xulei Yang, Zeng Zeng, Cuntai Guan, Wai Leong Tam, XiaoLi Li

Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis.

Graph Learning

Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation

no code implementations11 May 2023 Ziyuan Zhao, Fangcheng Zhou, Zeng Zeng, Cuntai Guan, S. Kevin Zhou

To achieve efficient few-shot cross-modality segmentation, we propose a novel transformation-consistent meta-hallucination framework, meta-hallucinator, with the goal of learning to diversify data distributions and generate useful examples for enhancing cross-modality performance.

Cardiac Segmentation Hallucination +6

Interpretable and Robust AI in EEG Systems: A Survey

no code implementations21 Apr 2023 Xinliang Zhou, Chenyu Liu, Liming Zhai, Ziyu Jia, Cuntai Guan, Yang Liu

In this paper, we present the first comprehensive survey and summarize the interpretable and robust AI techniques for EEG systems.

EEG

MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

no code implementations28 Mar 2023 Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo, Xulei Yang, Cuntai Guan

Our method demonstrates promising segmentation performance with a mean Dice score of 83. 8% and 81. 4% and an average asymmetric surface distance (ASSD) of 0. 55 mm and 0. 26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.

Ensemble Learning Image Segmentation +4

Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance

1 code implementation22 Feb 2023 Ce Ju, Reinmar Josef Kobler, Cuntai Guan

In order to synthesize these spatial covariance matrices and facilitate future improvements of geometric deep learning classifiers, we propose a generative modeling technique based on state-of-the-art score-based models.

EEG Electroencephalogram (EEG)

LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation

1 code implementation5 Dec 2022 Ziyuan Zhao, Fangcheng Zhou, Kaixin Xu, Zeng Zeng, Cuntai Guan, S. Kevin Zhou

To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.

Image Segmentation Medical Image Segmentation +4

Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition

no code implementations21 Nov 2022 Mengjiao Hu, Xudong Jiang, Kang Sim, Juan Helen Zhou, Cuntai Guan

Deep learning has been successfully applied to recognizing both natural images and medical images.

Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective from the Time-Frequency Analysis

1 code implementation25 Oct 2022 Ce Ju, Cuntai Guan

The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG).

Classification EEG +2

A Transformer-based deep neural network model for SSVEP classification

1 code implementation9 Oct 2022 Jianbo Chen, Yangsong Zhang, Yudong Pan, Peng Xu, Cuntai Guan

The proposed model validates the feasibility of deep learning models based on Transformer structure for SSVEP classification task, and could serve as a potential model to alleviate the calibration procedure in the practical application of SSVEP-based BCI systems.

Classification EEG +1

Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

2 code implementations13 Aug 2022 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative representations by our proposed contextual contrasting module.

Contrastive Learning Data Augmentation +5

ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation

1 code implementation5 Jul 2022 Ziyuan Zhao, Andong Zhu, Zeng Zeng, Bharadwaj Veeravalli, Cuntai Guan

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, especially in clinical practice.

Image Segmentation Knowledge Distillation +3

Self-supervised Assisted Active Learning for Skin Lesion Segmentation

1 code implementation14 May 2022 Ziyuan Zhao, Wenjing Lu, Zeng Zeng, Kaixin Xu, Bharadwaj Veeravalli, Cuntai Guan

Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements.

Active Learning Image Segmentation +5

Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3

1 code implementation24 Mar 2022 Su Zhang, Ruyi An, Yi Ding, Cuntai Guan

The visual encoding from the visual block is concatenated with the attention feature to emphasize the visual information.

Emotion Recognition

MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels

1 code implementation23 Mar 2022 Ziyuan Zhao, Kaixin Xu, Shumeng Li, Zeng Zeng, Cuntai Guan

Although deep unsupervised domain adaptation (UDA) can leverage well-established source domain annotations and abundant target domain data to facilitate cross-modality image segmentation and also mitigate the label paucity problem on the target domain, the conventional UDA methods suffer from severe performance degradation when source domain annotations are scarce.

Image Segmentation Medical Image Segmentation +3

Federated Graph Neural Networks: Overview, Techniques and Challenges

no code implementations15 Feb 2022 Rui Liu, Pengwei Xing, Zichao Deng, Anran Li, Cuntai Guan, Han Yu

This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs).

Federated Learning

Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification

1 code implementation5 Feb 2022 Ce Ju, Cuntai Guan

The mainstream DL methodology for the MI-EEG classification exploits the temporospatial patterns of EEG signals using convolutional neural networks (CNNs), which have remarkably succeeded in visual images.

Classification EEG +1

Deep Optimal Transport for Domain Adaptation on SPD Manifolds

1 code implementation15 Jan 2022 Ce Ju, Cuntai Guan

In recent years, there has been significant interest in solving the domain adaptation (DA) problem on symmetric positive definite (SPD) manifolds within the machine learning community.

Domain Adaptation Electroencephalogram (EEG) +1

Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models

no code implementations29 Sep 2021 Davide Coppola, Hwee Kuan Lee, Cuntai Guan

Experiments on the CIFAR10 dataset showed that using only $10\%$ of the full training set, the proposed method was able to adequately defend the model against the AutoPGD attack while maintaining a classification accuracy on clean images outperforming the model with adversarial training by $7\%$.

Adversarial Robustness Image Classification

ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training

1 code implementation9 Jul 2021 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.

Automatic Sleep Stage Classification Domain Adaptation +2

Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion

1 code implementation2 Jul 2021 Su Zhang, Yi Ding, Ziquan Wei, Cuntai Guan

We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an aural block containing several parallel TCNs; and (3) a leader-follower attentive fusion block combining the audio-visual information.

Emotion Recognition

Time-Series Representation Learning via Temporal and Contextual Contrasting

1 code implementation26 Jun 2021 Emadeldeen Eldele, Mohamed Ragab, Zhenghua Chen, Min Wu, Chee Keong Kwoh, XiaoLi Li, Cuntai Guan

In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data.

Automatic Sleep Stage Classification Contrastive Learning +9

LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface

1 code implementation5 May 2021 Yi Ding, Neethu Robinson, Chengxuan Tong, Qiuhao Zeng, Cuntai Guan

It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers.

EEG EEG Emotion Recognition +1

An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG

1 code implementation28 Apr 2021 Emadeldeen Eldele, Zhenghua Chen, Chengyu Liu, Min Wu, Chee-Keong Kwoh, XiaoLi Li, Cuntai Guan

The MRCNN can extract low and high frequency features and the AFR is able to improve the quality of the extracted features by modeling the inter-dependencies between the features.

Automatic Sleep Stage Classification EEG +2

TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition

2 code implementations7 Apr 2021 Yi Ding, Neethu Robinson, Su Zhang, Qiuhao Zeng, Cuntai Guan

TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously.

EEG Emotion Recognition

FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface

1 code implementation17 Mar 2021 Ravikiran Mane, Effie Chew, Karen Chua, Kai Keng Ang, Neethu Robinson, A. P. Vinod, Seong-Whan Lee, Cuntai Guan

With this design, we compare FBCNet with state-of-the-art (SOTA) BCI algorithm on four MI datasets: The BCI competition IV dataset 2a (BCIC-IV-2a), the OpenBMI dataset, and two large datasets from chronic stroke patients.

Binary Classification Classification +3

DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

1 code implementation22 Jan 2021 Ziyuan Zhao, Zeng Zeng, Kaixin Xu, Cen Chen, Cuntai Guan

We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform.

Active Learning Ensemble Learning +2

Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping

1 code implementation10 Oct 2020 Hong Jing Khok, Victor Teck Chang Koh, Cuntai Guan

Our model is able to perform on a calibration-free user-independent scenario, which is desirable for clinical diagnostics.

Electroencephalogram (EEG) Multi-Task Learning

Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset

2 code implementations7 Sep 2020 Erico Tjoa, Cuntai Guan

Heatmaps can be appealing due to the intuitive and visual ways to understand them but assessing their qualities might not be straightforward.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Federated Transfer Learning for EEG Signal Classification

1 code implementation26 Apr 2020 Ce Ju, Dashan Gao, Ravikiran Mane, Ben Tan, Yang Liu, Cuntai Guan

The success of deep learning (DL) methods in the Brain-Computer Interfaces (BCI) field for classification of electroencephalographic (EEG) recordings has been restricted by the lack of large datasets.

Classification Domain Adaptation +6

TSception: A Deep Learning Framework for Emotion Detection Using EEG

1 code implementation2 Apr 2020 Yi Ding, Neethu Robinson, Qiuhao Zeng, Duo Chen, Aung Aung Phyo Wai, Tih-Shih Lee, Cuntai Guan

TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously.

EEG Electroencephalogram (EEG) +1

Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls

no code implementations14 Mar 2020 Mengjiao Hu, Kang Sim, Juan Helen Zhou, Xudong Jiang, Cuntai Guan

Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but not yet been applied to differentiating patients with schizophrenia from healthy controls.

BIG-bench Machine Learning General Classification

Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)

no code implementations4 Feb 2020 Aung Aung Phyo Wai, Yangsong Zhang, Heng Guo, Ying Chi, Lei Zhang, Xian-Sheng Hua, Seong Whan Lee, Cuntai Guan

We observed that CSTA achieves the maximum mean accuracy of 97. 43$\pm$2. 26 % and 85. 71$\pm$13. 41 % with four-class and forty-class SSVEP data-sets respectively in sub-second response time in offline analysis.

Enhancing the Extraction of Interpretable Information for Ischemic Stroke Imaging from Deep Neural Networks

1 code implementation19 Nov 2019 Erico Tjoa, Guo Heng, Lu Yuhao, Cuntai Guan

We implement a visual interpretability method Layer-wise Relevance Propagation (LRP) on top of 3D U-Net trained to perform lesion segmentation on the small dataset of multi-modal images provided by ISLES 2017 competition.

Lesion Segmentation

Machine learning driven synthesis of few-layered WTe2

no code implementations10 Oct 2019 Manzhang Xu, Bijun Tang, Yuhao Lu, Chao Zhu, Lu Zheng, Jingyu Zhang, Nannan Han, Yuxi Guo, Jun Di, Pin Song, Yongmin He, Lixing Kang, Zhiyong Zhang, Wu Zhao, Cuntai Guan, Xuewen Wang, Zheng Liu

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 applications but also for the exploration of fundamental physical properties.

BIG-bench Machine Learning

A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI

no code implementations17 Jul 2019 Erico Tjoa, Cuntai Guan

Unfortunately, the blackbox nature of the deep learning is still unresolved, and many machine decisions are still poorly understood.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI)

Machine learning-guided synthesis of advanced inorganic materials

1 code implementation10 May 2019 Bijun Tang, Yuhao Lu, Jiadong Zhou, Han Wang, Prafful Golani, Manzhang Xu, Quan Xu, Cuntai Guan, Zheng Liu

Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.

BIG-bench Machine Learning

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