Zobrazeno 1 - 10
of 879
pro vyhledávání: '"Wang Xiangfeng"'
Autor:
YAO Dong, HE Yiqi, ZHANG Qin, LIU Changhua, LI Taibiao, YAO Caiming, WANG Xiangfeng, WANG Qinfeng, HU Hongjun
Publikováno v:
康复学报, Pp 1-7 (2024)
ObjectiveTo explore the feasibility of the International Classification of Functioning, Disability and Health Rehabilitation Set (ICF-RS) in evaluating the overall functional level, rehabilitation treatment effect, and the relationship between rehabi
Externí odkaz:
https://doaj.org/article/399377ef24774128bf1281090525348d
Publikováno v:
康复学报, Vol 33, Pp 325-332 (2023)
ObjectiveTo observe the effect of electroacupuncture (EA) prestimulation with mental tri-needles on the inflammatory response of hippocampus in aged rats with perioperative neurocognitive disorders (PND).MethodsA total of 24 aged male SD rats were ra
Externí odkaz:
https://doaj.org/article/9f8ed69ab9274edfa20a7ec7f08ee858
Publikováno v:
Geocarto International, Vol 39, Iss 1 (2024)
Aiming at the existing deep learning classification model for power corridor point cloud still need to improve the classification efficiency and the robustness of the classification model to meet the requirements of practical applications. An improve
Externí odkaz:
https://doaj.org/article/9a72391c99c84a64b047c738155ed439
Autor:
He, Yuchen, Wang, Xiangfeng
Federated learning is a specific distributed learning paradigm in which a central server aggregates updates from multiple clients' local models, thereby enabling the server to learn without requiring clients to upload their private data, maintaining
Externí odkaz:
http://arxiv.org/abs/2411.01916
Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usua
Externí odkaz:
http://arxiv.org/abs/2411.01904
Interactive medical image segmentation (IMIS) has shown significant potential in enhancing segmentation accuracy by integrating iterative feedback from medical professionals. However, the limited availability of enough 3D medical data restricts the g
Externí odkaz:
http://arxiv.org/abs/2408.02635
With the rising popularity of Transformer-based large language models (LLMs), reducing their high inference costs has become a significant research focus. One effective approach is to compress the long input contexts. Existing methods typically lever
Externí odkaz:
http://arxiv.org/abs/2406.13618
Autor:
Shen, Chuyun, Li, Wenhao, Chen, Haoqing, Wang, Xiaoling, Zhu, Fengping, Li, Yuxin, Wang, Xiangfeng, Jin, Bo
Radiologists must utilize multiple modal images for tumor segmentation and diagnosis due to the limitations of medical imaging and the diversity of tumor signals. This leads to the development of multimodal learning in segmentation. However, the redu
Externí odkaz:
http://arxiv.org/abs/2401.02717
Autor:
Sheng, Junjie, Huang, Zixiao, Shen, Chuyun, Li, Wenhao, Hua, Yun, Jin, Bo, Zha, Hongyuan, Wang, Xiangfeng
The formidable capacity for zero- or few-shot decision-making in language agents encourages us to pose a compelling question: Can language agents be alternatives to PPO agents in traditional sequential decision-making tasks? To investigate this, we f
Externí odkaz:
http://arxiv.org/abs/2312.03290
The Segmentation Anything Model (SAM) has recently emerged as a foundation model for addressing image segmentation. Owing to the intrinsic complexity of medical images and the high annotation cost, the medical image segmentation (MIS) community has b
Externí odkaz:
http://arxiv.org/abs/2306.08958