1 code implementation • 30 Aug 2023 • Yi Ding, Su Zhang, Chuangao Tang, Cuntai Guan
A natural method is to learn the temporal dynamic patterns.
no code implementations • 14 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.
no code implementations • 15 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.
no code implementations • 11 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.
no code implementations • 21 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.
no code implementations • 28 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.
1 code implementation • 22 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.
1 code implementation • 5 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.
no code implementations • 21 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.
1 code implementation • 25 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).
1 code implementation • 9 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.
2 code implementations • 13 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.
1 code implementation • 25 Jul 2022 • Amrest Chinkamol, Vetit Kanjaras, Phattarapong Sawangjai, Yitian Zhao, Thapanun Sudhawiyangkul, Chantana Chantrapornchai, Cuntai Guan, Theerawit Wilaiprasitporn
In this work, we propose the application of the scribble-base weakly-supervised learning method to automate the pixel-level annotation.
Ranked #1 on Retinal Vessel Segmentation on ROSE-2
1 code implementation • 5 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.
1 code implementation • 5 Jul 2022 • Ziyuan Zhao, Jinxuan Hu, Zeng Zeng, Xulei Yang, Peisheng Qian, Bharadwaj Veeravalli, Cuntai Guan
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation.
1 code implementation • 14 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.
1 code implementation • 24 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.
1 code implementation • 23 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.
no code implementations • 15 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).
1 code implementation • 5 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.
1 code implementation • 15 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.
no code implementations • 29 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 $7\%$.
1 code implementation • 9 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.
1 code implementation • 2 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.
1 code implementation • 26 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.
Ranked #1 on Recognizing And Localizing Human Actions on HAR
Automatic Sleep Stage Classification Contrastive Learning +9
1 code implementation • 21 May 2021 • Shumeng Li, Ziyuan Zhao, Kaixin Xu, Zeng Zeng, Cuntai Guan
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
1 code implementation • 5 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.
1 code implementation • 28 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.
Ranked #1 on Automatic Sleep Stage Classification on Sleep-EDF
2 code implementations • 7 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.
1 code implementation • 17 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.
1 code implementation • 7 Feb 2021 • Phairot Autthasan, Rattanaphon Chaisaen, Thapanun Sudhawiyangkul, Phurin Rangpong, Suktipol Kiatthaveephong, Nat Dilokthanakul, Gun Bhakdisongkhram, Huy Phan, Cuntai Guan, Theerawit Wilaiprasitporn
We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.
1 code implementation • 22 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.
1 code implementation • 10 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.
Ranked #1 on Electroencephalogram (EEG) on HS-SSVEP
2 code implementations • 7 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)
1 code implementation • 26 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.
1 code implementation • 2 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.
no code implementations • 14 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.
no code implementations • 4 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.
1 code implementation • 19 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.
no code implementations • 10 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.
no code implementations • 17 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)
1 code implementation • 10 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.