502 citations
- Shenzhen UniversityCN15 papers
- Chinese Academy of SciencesCN5 papers
- Chinese University of Hong KongHK3 papers
- Chinese University of Hong Kong, ShenzhenCN3 papers
- Nanjing Medical UniversityCN3 papers
- Shenzhen Institutes of Advanced TechnologyCN3 papers
- Southeast UniversityCN3 papers
- Southern University of Science and TechnologyCN3 papers
- University of LeedsGB3 papers
- Jinan UniversityCN2 papers
- Shenzhen Luohu People's HospitalCN2 papers
- Shenzhen Research Institute of Big DataCN2 papers
24 papers
Masked Generative-Contrastive Representation Learning for Cross-Dataset EEG-Based Emotion Recognition
Huqin Weng, Jiayang Huang, Yimin Wen +3
Self-supervised learning (SSL) shows strong potential for cross-dataset transfer by improving feature representation and generalization. However, its application to EEG-based emoti…
FetalFlex: Anatomy-Guided Diffusion Model for Flexible Control on Fetal Ultrasound Image Synthesis
Yaofei Duan, Tao Tan, Zhiyuan Zhu +14
Fetal ultrasound (US) examinations require the acquisition of multiple planes, each providing unique diagnostic information to evaluate fetal development and screening for congenit…
From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning
Yuncheng Jiang, Chun-Mei Feng, Jinke Ren +15
Ultrasound imaging is widely used in clinical diagnosis due to its non-invasive nature and real-time capabilities. However, traditional ultrasound diagnostics relies heavily on phy…
Advances in Photoacoustic Imaging Reconstruction and Quantitative Analysis for Biomedical Applications
Lei Wang, Weiming Zeng, Kai Long +5
Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring e…
SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation
Xinyu Xiong, Zihuang Wu, Shuangyi Tan +6
Image segmentation plays an important role in vision understanding. Recently, the emerging vision foundation models continuously achieved superior performance on various tasks. Fol…
CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation
Weiheng Yao, Zhihan Lyu, Mufti Mahmud +3
Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, th…