83 citations · 159 across the 6 of their papers we have counts for
7 papers
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…
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…
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…