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Found 42 papers, 30 papers with code
MASA-TCN: Multi-anchor Space-aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition
A natural method is to learn the temporal dynamic patterns.
Aggregating Intrinsic Information to Enhance BCI Performance through Federated Learning
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.
SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction
Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis.
Meta-hallucinator: Towards Few-Shot Cross-Modality Cardiac Image Segmentation
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.
Interpretable and Robust AI in EEG Systems: A Survey
In this paper, we present the first comprehensive survey and summarize the interpretable and robust AI techniques for EEG systems.
MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
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.
Score-Based Data Generation for EEG Spatial Covariance Matrices: Towards Boosting BCI Performance
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.
LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation
To assess the effectiveness of our method, we conduct extensive experiments on two different tasks for cross-modality segmentation between MRI and CT images.
Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition
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
The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces based on electroencephalography (EEG).
A Transformer-based deep neural network model for SSVEP classification
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.
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
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.
OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation
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
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
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.
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation.
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements.
Continuous Emotion Recognition using Visual-audio-linguistic information: A Technical Report for ABAW3
The visual encoding from the visual block is concatenated with the attention feature to emphasize the visual information.
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels
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.
Federated Graph Neural Networks: Overview, Techniques and Challenges
This has led to the rapid development of the emerging research field of federated graph neural networks (FedGNNs).
Tensor-CSPNet: A Novel Geometric Deep Learning Framework for Motor Imagery Classification
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.
Deep Optimal Transport for Domain Adaptation on SPD Manifolds
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.
Use of small auxiliary networks and scarce data to improve the adversarial robustness of deep learning models
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 $7\%$.
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
Second, we design an iterative self-training strategy to improve the classification performance on the target domain via target domain pseudo labels.
Continuous Emotion Recognition with Audio-visual Leader-follower Attentive Fusion
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.
Time-Series Representation Learning via Temporal and Contextual Contrasting
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
Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation
Deep learning has achieved promising segmentation performance on 3D left atrium MR images.
LGGNet: Learning from Local-Global-Graph Representations for Brain-Computer Interface
It captures temporal dynamics of EEG which then serves as input to the proposed local and global graph-filtering layers.
An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG
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
TSception: Capturing Temporal Dynamics and Spatial Asymmetry from EEG for Emotion Recognition
TSception consists of dynamic temporal, asymmetric spatial, and high-level fusion layers, which learn discriminative representations in the time and channel dimensions simultaneously.
FBCNet: A Multi-view Convolutional Neural Network for Brain-Computer Interface
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.
MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification simultaneously.
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation
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.
Deep Multi-Task Learning for SSVEP Detection and Visual Response Mapping
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
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset
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
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.
TSception: A Deep Learning Framework for Emotion Detection Using EEG
TSception consists of temporal and spatial convolutional layers, which learn discriminative representations in the time and channel domains simultaneously.
Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls
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.
Towards a Fast Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI)
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
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.
Machine learning driven synthesis of few-layered WTe2
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.
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI
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
Synthesis of advanced inorganic materials with minimum number of trials is of paramount importance towards the acceleration of inorganic materials development.
