Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Chuyao Feng"'
Publikováno v:
BMC Genomics, Vol 24, Iss 1, Pp 1-7 (2023)
Abstract Background The association between breast cancer (BC) and thyroid cancer (TC) has been studied in several epidemiological studies. However, the underlying causal relationship between them is not yet clear. Methods The data from the latest la
Externí odkaz:
https://doaj.org/article/ca3d499d82c746ac8d63a2633ada556d
Publikováno v:
Frontiers in Endocrinology, Vol 14 (2023)
BackgroundPapillary thyroid cancer (PTC) is the most common endocrine malignancy worldwide. The treatment of PTC has attracted extensive attention and discussion from the public and scholars. However, no article has systematically assessed the relate
Externí odkaz:
https://doaj.org/article/9b9bb33d88554879a8a2fa85bfb91d3b
Autor:
Xueqi Zhang, Fan Zhang, Qiuxian Li, Renaguli Aihaiti, Chuyao Feng, Deshi Chen, Xu Zhao, Weiping Teng
Publikováno v:
Frontiers in Endocrinology, Vol 13 (2022)
BackgroundThe effect of iodine on papillary thyroid cancer (PTC) has been controversial for many years. Since urinary iodine is an effective indicator of iodine intake, some recent epidemiological studies have described the relationship between urina
Externí odkaz:
https://doaj.org/article/fbd15b5c98964198af104a1b8d86fc79
Publikováno v:
Frontiers in Nutrition, Vol 9 (2022)
Thyroid cancer (TC) is the most frequent endocrine malignancy. The incidence of TC, especially papillary thyroid carcinoma (PTC), has continued to rise all over the world during the past few years, for reasons that are not entirely clear. Though the
Externí odkaz:
https://doaj.org/article/be91f9e2c35f45fda6094f1b8b66539b
Publikováno v:
IEEE Access, Vol 9, Pp 77067-77078 (2021)
Due to the limited training data, current data-driven algorithms, including deep convolutional networks (DCNs), are susceptible to training data that cannot be applied to new data directly. Unlike existing methods that are trying to improve model gen
Externí odkaz:
https://doaj.org/article/fcf01859c92542e3b1c72b651813b776
Autor:
Fan Zhang, Yongze Li, Xiaohui Yu, Xichang Wang, Zheyu Lin, Bo Song, Lijun Tian, Chuyao Feng, Zhongyan Shan, Weiping Teng
Publikováno v:
Frontiers in Endocrinology, Vol 12 (2021)
BackgroundMetabolic syndrome (MetS) has a potential connection with thyroid disease, but its relationship with thyroid nodules (TNs) is still controversial. This study aims to clarify the relationship between MetS and TNs, and this relationship in th
Externí odkaz:
https://doaj.org/article/495c23fc87b041a580c995ddf6382e4b
Publikováno v:
IEEE Access, Vol 9, Pp 77067-77078 (2021)
Due to the limited training data, current data-driven algorithms, including deep convolutional networks (DCNs), are susceptible to training data that cannot be applied to new data directly. Unlike existing methods that are trying to improve model gen
Autor:
Lijun Tian, Xiaohui Yu, Chuyao Feng, Weiping Teng, Zheyu Lin, Zhongyan Shan, Bo Song, Fan Zhang, Xichang Wang, Yongze Li
Publikováno v:
Frontiers in Endocrinology, Vol 12 (2021)
Frontiers in Endocrinology
Frontiers in Endocrinology
BackgroundMetabolic syndrome (MetS) has a potential connection with thyroid disease, but its relationship with thyroid nodules (TNs) is still controversial. This study aims to clarify the relationship between MetS and TNs, and this relationship in th
Publikováno v:
International Journal of Environmental Research and Public Health; Volume 19; Issue 19; Pages: 12351
The long-term mortality risk of natural disasters is a key threat to disaster resilience improvement, yet an authoritative certification and a reliable surveillance system are, unfortunately, yet to be established in many countries. This study aimed
Publikováno v:
ICASSP
Recently, many attention-based deep neural networks have emerged and achieved state-of-the-art performance in environmental sound classification. The essence of attention mechanism is assigning contribution weights on different parts of features, nam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::502841d68eea20c032de8d506616280e
http://arxiv.org/abs/2011.02561
http://arxiv.org/abs/2011.02561