Zobrazeno 1 - 10
of 379
pro vyhledávání: '"Liu Zelong"'
Publikováno v:
Guan'gai paishui xuebao, Vol 43, Iss 11, Pp 18-26 (2024)
【Objective】 Mulched drip irrigation is a widely used technique in Northwestern China. This paper investigates the combined effects of mulched drip irrigation and nitrogen fertilization on the growth and yield of summer maize. 【Method】 The exp
Externí odkaz:
https://doaj.org/article/f6cecad9fe264efc814b9ba6ba86af43
Publikováno v:
中国工程科学, Vol 24, Iss 6, Pp 72-80 (2022)
The food nutrition and health industry is an important component of the Healthy China strategy in the context of dual circulation. This study summarized the development trend of the industry and analyzed the challenges faced by the industry under the
Externí odkaz:
https://doaj.org/article/f597fd55269a484bbd164b64e4e53a96
Medical anomaly detection is a critical research area aimed at recognizing abnormal images to aid in diagnosis.Most existing methods adopt synthetic anomalies and image restoration on normal samples to detect anomaly. The unlabeled data consisting of
Externí odkaz:
http://arxiv.org/abs/2405.12872
Autor:
Liu, Zelong, Tieu, Andrew, Patel, Nikhil, Zhou, Alexander, Soultanidis, George, Fayad, Zahi A., Deyer, Timothy, Mei, Xueyan
Artificial Intelligence (AI) has the potential to revolutionize diagnosis and segmentation in medical imaging. However, development and clinical implementation face multiple challenges including limited data availability, lack of generalizability, an
Externí odkaz:
http://arxiv.org/abs/2402.01034
Autor:
Zhou, Alexander, Liu, Zelong, Tieu, Andrew, Patel, Nikhil, Sun, Sean, Yang, Anthony, Choi, Peter, Fauveau, Valentin, Soultanidis, George, Huang, Mingqian, Doshi, Amish, Fayad, Zahi A., Deyer, Timothy, Mei, Xueyan
Purpose To develop a deep learning model for multi-anatomy and many-class segmentation of diverse anatomic structures on MRI imaging. Materials and Methods In this retrospective study, two datasets were curated and annotated for model development and
Externí odkaz:
http://arxiv.org/abs/2402.01031
Autor:
Liu, Zelong, Zhou, Alexander, Yang, Arnold, Yilmaz, Alara, Yoo, Maxwell, Sullivan, Mikey, Zhang, Catherine, Grant, James, Li, Daiqing, Fayad, Zahi A., Huver, Sean, Deyer, Timothy, Mei, Xueyan
Deep learning in medical imaging often requires large-scale, high-quality data or initiation with suitably pre-trained weights. However, medical datasets are limited by data availability, domain-specific knowledge, and privacy concerns, and the creat
Externí odkaz:
http://arxiv.org/abs/2312.05953
Autor:
Pinaya, Walter H. L., Graham, Mark S., Kerfoot, Eric, Tudosiu, Petru-Daniel, Dafflon, Jessica, Fernandez, Virginia, Sanchez, Pedro, Wolleb, Julia, da Costa, Pedro F., Patel, Ashay, Chung, Hyungjin, Zhao, Can, Peng, Wei, Liu, Zelong, Mei, Xueyan, Lucena, Oeslle, Ye, Jong Chul, Tsaftaris, Sotirios A., Dogra, Prerna, Feng, Andrew, Modat, Marc, Nachev, Parashkev, Ourselin, Sebastien, Cardoso, M. Jorge
Recent advances in generative AI have brought incredible breakthroughs in several areas, including medical imaging. These generative models have tremendous potential not only to help safely share medical data via synthetic datasets but also to perfor
Externí odkaz:
http://arxiv.org/abs/2307.15208
Publikováno v:
In Energy 1 December 2024 311
Autor:
Xia, Runtian, Wang, Naixin, Cai, Xinheng, Yang, Jing, Liu, Hongyu, Liu, Zelong, Liu, Haomiao, Zhang, Qundan, Tao, Zhiping, Wang, Wei
Publikováno v:
In Fuel 1 February 2025 381 Part D
Publikováno v:
In Microporous and Mesoporous Materials 15 January 2025 382