Zobrazeno 1 - 10
of 289
pro vyhledávání: '"Zeng, Weiming"'
Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array
Externí odkaz:
http://arxiv.org/abs/2411.02843
Current visual question answering (VQA) tasks often require constructing multimodal datasets and fine-tuning visual language models, which demands significant time and resources. This has greatly hindered the application of VQA to downstream tasks, s
Externí odkaz:
http://arxiv.org/abs/2411.01445
Recent advancements in synthetic aperture radar (SAR) ship detection using deep learning have significantly improved accuracy and speed, yet effectively detecting small objects in complex backgrounds with fewer parameters remains a challenge. This le
Externí odkaz:
http://arxiv.org/abs/2410.23073
Autor:
Chen, Hongyu, Zeng, Weiming, Chen, Chengcheng, Cai, Luhui, Wang, Fei, Wang, Lei, Zhang, Wei, Li, Yueyang, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
In the fields of affective computing (AC) and brain-machine interface (BMI), the analysis of physiological and behavioral signals to discern individual emotional states has emerged as a critical research frontier. While deep learning-based approaches
Externí odkaz:
http://arxiv.org/abs/2410.00166
Although developed functional magnetic resonance imaging (fMRI) registration algorithms based on deep learning have achieved a certain degree of alignment of functional area, they underutilized fine structural information. In this paper, we propose a
Externí odkaz:
http://arxiv.org/abs/2409.17569
Autor:
Zhang, Wei, Zeng, Weiming, Chen, Hongyu, Liu, Jie, Yan, Hongjie, Zhang, Kaile, Tao, Ran, Siok, Wai Ting, Wang, Nizhuan
Accurate diagnosis of depression is crucial for timely implementation of optimal treatments, preventing complications and reducing the risk of suicide. Traditional methods rely on self-report questionnaires and clinical assessment, lacking objective
Externí odkaz:
http://arxiv.org/abs/2407.21323
Autor:
Feng, Yu, Zeng, Weiming, Xie, Yifan, Chen, Hongyu, Wang, Lei, Wang, Yingying, Yan, Hongjie, Zhang, Kaile, Tao, Ran, Siok, Wai Ting, Wang, Nizhuan
Background: Although it has been noticed that depressed patients show differences in processing emotions, the precise neural modulation mechanisms of positive and negative emotions remain elusive. FMRI is a cutting-edge medical imaging technology ren
Externí odkaz:
http://arxiv.org/abs/2407.18492
Autor:
Li, Yueyang, Zeng, Weiming, Dong, Wenhao, Han, Di, Chen, Lei, Chen, Hongyu, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
Single-channel electroencephalogram (EEG) is a cost-effective, comfortable, and non-invasive method for monitoring brain activity, widely adopted by researchers, consumers, and clinicians. The increasing number and proportion of articles on single-ch
Externí odkaz:
http://arxiv.org/abs/2407.14850
Autor:
Li, Yueyang, Zeng, Weiming, Dong, Wenhao, Cai, Luhui, Wang, Lei, Chen, Hongyu, Yan, Hongjie, Bian, Lingbin, Wang, Nizhuan
Background: Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ s
Externí odkaz:
http://arxiv.org/abs/2407.03217
Autor:
Chen, Hongyu, Zeng, Weiming, Cai, Luhui, Wang, Lei, Lu, Jia, Li, Yueyang, Yan, Hongjie, Siok, Wai Ting, Wang, Nizhuan
High-precision acquisition of dense-channel electroencephalogram (EEG) signals is often impeded by the costliness and lack of portability of equipment. In contrast, generating dense-channel EEG signals effectively from sparse channels shows promise a
Externí odkaz:
http://arxiv.org/abs/2406.15269