Zobrazeno 1 - 10
of 1 959
pro vyhledávání: '"Yao, Shun"'
Autor:
Li, Chenjun, Yang, Dian, Yao, Shun, Wang, Shuyue, Wu, Ye, Zhang, Le, Li, Qiannuo, Cho, Kang Ik Kevin, Seitz-Holland, Johanna, Ning, Lipeng, Legarreta, Jon Haitz, Rathi, Yogesh, Westin, Carl-Fredrik, O'Donnell, Lauren J., Sochen, Nir A., Pasternak, Ofer, Zhang, Fan
In this study, we developed an Evidence-based Ensemble Neural Network, namely EVENet, for anatomical brain parcellation using diffusion MRI. The key innovation of EVENet is the design of an evidential deep learning framework to quantify predictive un
Externí odkaz:
http://arxiv.org/abs/2409.07020
Autor:
Chuang, Yao-Shun, Lee, Chun-Teh, Tokede, Oluwabunmi, Lin, Guo-Hao, Brandon, Ryan, Tran, Trung Duong, Jiang, Xiaoqian, Walji, Muhammad F.
This research addresses the issue of missing structured data in dental records by extracting diagnostic information from unstructured text. The updated periodontology classification system's complexity has increased incomplete or missing structured d
Externí odkaz:
http://arxiv.org/abs/2407.21050
This study examines integrating EHRs and NLP with large language models (LLMs) to improve healthcare data management and patient care. It focuses on using advanced models to create secure, HIPAA-compliant synthetic patient notes for biomedical resear
Externí odkaz:
http://arxiv.org/abs/2407.16166
Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have no
Externí odkaz:
http://arxiv.org/abs/2405.05564
For sharing privacy-sensitive data, de-identification is commonly regarded as adequate for safeguarding privacy. Synthetic data is also being considered as a privacy-preserving alternative. Recent successes with numerical and tabular data generative
Externí odkaz:
http://arxiv.org/abs/2402.00179
Autor:
Chuang, Yao-Shun, Jiang, Xiaoqian, Lee, Chun-Teh, Brandon, Ryan, Tran, Duong, Tokede, Oluwabunmi, Walji, Muhammad F.
This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as t
Externí odkaz:
http://arxiv.org/abs/2311.10810
Autor:
Chuang, Yao-Shun, Lee, Chun-Teh, Brandon, Ryan, Tran, Trung Duong, Tokede, Oluwabunmi, Walji, Muhammad F., Jiang, Xiaoqian
This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expressi
Externí odkaz:
http://arxiv.org/abs/2311.10809
Autor:
Du, Xingyu, Idjadi, Mohamad Hossein, Ding, Yixiao, Zhang, Tao, Geers, Alexander J., Yao, Shun, Pyo, Jun Beom, Aflatouni, Firooz, Allen, Mark, Olsson III, Roy H.
A single tunable filter simplifies complexity, reduces insertion loss, and minimizes size compared to frequency switchable filter banks commonly used for radio frequency (RF) band selection. Magnetostatic wave (MSW) filters stand out for their wide,
Externí odkaz:
http://arxiv.org/abs/2308.00907
Autor:
Li, Sipei, He, Jianzhong, Xue, Tengfei, Xie, Guoqiang, Yao, Shun, Chen, Yuqian, Torio, Erickson F., Feng, Yuanjing, Bastos, Dhiego CA, Rathi, Yogesh, Makris, Nikos, Kikinis, Ron, Bi, Wenya Linda, Golby, Alexandra J, O'Donnell, Lauren J, Zhang, Fan
The retinogeniculate pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can info
Externí odkaz:
http://arxiv.org/abs/2211.08119
Autor:
Xu, Liang1,2 (AUTHOR), Yao, Shun3 (AUTHOR) yaosh23@mail.sysu.edu.cn, Ding, Yifan Evan4 (AUTHOR), Xie, Mengxiao5 (AUTHOR), Feng, Dingqi6 (AUTHOR), Sha, Pengfei1 (AUTHOR), Tan, Lu1,2 (AUTHOR), Bei, Fengfeng4 (AUTHOR) fbei@bwh.harvard.edu, Yao, Yizheng1 (AUTHOR) yzyao28@suda.edu.cn
Publikováno v:
Journal of Translational Medicine. 9/27/2024, Vol. 22 Issue 1, p1-17. 17p.