论文汇总

Wang X, Zhao K, Yu S, et al. ST-GF:Graph-based Fusion of Spatial and Temporal Features for EEG Motor Imagery Decoding. //2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024.
Accepted.

Yuan Q, Shi E, Zhao K, et al. A Smooth Conditional Domain Adversarial Training Framework for EEG Motor Imagery Decoding. //2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024.
Accepted.

Yue C, Hu H, Shi E, et al. TF-HiTNet:A Temporal-Frequency Hierarchical Transformer Network for EEG Motor Imagery Classification. //2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024.
Accepted.

Zhang X, Shi E, Yu S, et al. DTCA:Dual-Branch Transformer with Cross-Attention for EEG and Eye Movement Data Fusion[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham:Springer Nature Switzerland, 2024:141-151.
https://link.springer.com/chapter/10.1007/978-3-031-72069-7_14

Zhao K, Kang Y, Wu J, et al. A Self-Adaptive Subgraph Generation Algorithm for EEG Channel Selection[C]//2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024:1-5.
https://ieeexplore.ieee.org/abstract/document/10635649

Wang J, Liu Z, Zhao L, et al. Review of large vision models and visual prompt engineering[J]. Meta-Radiology, 2023:100047.
https://www.sciencedirect.com/science/article/pii/S2950162823000474

Shi E, Yu S, Kang Y, et al. MEET:A Multi-Band EEG Transformer for Brain States Decoding[J]. IEEE Transactions on Biomedical Engineering, 2023.
https://ieeexplore.ieee.org/abstract/document/10345766

Hu H, Zhang H, Shi E, et al. A Diffusion-Based Multi-Objective Ant Colony Algorithm For Optimizing Network Topology Design. 2023 The 9th International Conference On Communication And Information Processing.
https://dl.acm.org/doi/abs/10.1145/3638884.3638945

Kang Y, Wang R, Shi E, et al. Exploring Brain Function-Structure Connectome Skeleton via Self-supervised Graph-Transformer Approach[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham:Springer Nature Switzerland, 2023:308-317.
https://link.springer.com/chapter/10.1007/978-3-031-43993-3_30

Zhang S, Wang R, Kang Y, et al. Joint Representation of Functional and Structural Profiles for Identifying Common and Consistent 3-Hinge Gyral Folding Landmark[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham:Springer Nature Switzerland, 2023:163-172.
https://link.springer.com/chapter/10.1007/978-3-031-43993-3_16

Zhang S, Zhang H, Wang R, et al. A Novel Multi-Modality Framework for Exploring Brain Connectivity Hubs Via Reinforcement Learning Approach[C]//2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023:1-5.
https://ieeexplore.ieee.org/abstract/document/10230789

Hu H, Yue C, Shi E, et al. Effective Human Motor Imagery Recognition via Segment Pool Based on One-Dimensional Convolutional Neural Network with Bidirectional Recurrent Attention Unit Network[J]. Applied Sciences, 2023, 13(16):9233.
https://www.mdpi.com/2076-3417/13/16/9233

Zhang S, Shi E, Wu L, et al. Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network[J]. Neural Networks, 2023, 165:1035-1049.
https://www.sciencedirect.com/science/article/abs/pii/S0893608023003520

Wang J, Shi E, Yu S, et al. Prompt engineering for healthcare:Methodologies and applications[J]. arXiv preprint arXiv:2304.14670, 2023.
https://arxiv.org/abs/2304.14670

Zhang S, Wu J, Shi E, et al. MM-GLCM-CNN:A multi-scale and multi-level based GLCM-CNN for polyp classification[J]. Computerized Medical Imaging and Graphics, 2023:102257.
https://www.sciencedirect.com/science/article/abs/pii/S0895611123000757

Zhang S, Kang Y, Yu S, et al. A Novel Two-Stage Multi-view Low-Rank Sparse Subspace Clustering Approach to Explore the Relationship Between Brain Function and Structure[C]//International Workshop on Machine Learning in Medical Imaging. Cham:Springer Nature Switzerland, 2022:191-200.
https://link.springer.com/chapter/10.1007/978-3-031-21014-3_20

Zhang S, Wu J, Yu S, et al. A Bagging Strategy-Based Multi-scale Texture GLCM-CNN Model for Differentiating Malignant from Benign Lesions Using Small Pathologically Proven Dataset[C]//International Workshop on Multiscale Multimodal Medical Imaging. Cham:Springer Nature Switzerland, 2022:44-53.
https://link.springer.com/chapter/10.1007/978-3-031-18814-5_5

Yu S, Shi E, Wang R, et al. A hybrid learning framework for fine-grained interpretation of brain spatiotemporal patterns during naturalistic functional magnetic resonance imaging[J]. Frontiers in Human Neuroscience, 2022, 16:944543.
https://www.frontiersin.org/articles/10.3389/fnhum.2022.944543/full