Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Xiang-hui Bai"'
Publikováno v:
Journal of Ovarian Research, Vol 17, Iss 1, Pp 1-12 (2024)
Abstract Objectives The study aimed to compare the diagnostic efficacy of the machine learning models with expert subjective assessment (SA) in assessing the malignancy risk of ovarian tumors using transvaginal ultrasound (TVUS). Methods The retrospe
Externí odkaz:
https://doaj.org/article/3fb817d6d8cb4569be726eb6a00f516c
Autor:
Hui Chen, Bo-Wen Yang, Le Qian, Yi-Shuang Meng, Xiang-Hui Bai, Xiao-Wei Hong, Xin He, Mei-Jiao Jiang, Fei Yuan, Qin-Wen Du, Wei-Wei Feng
Publikováno v:
Radiology. 304:106-113
Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c193d175f6bb796501ffa30c45b76b4b
https://doi.org/10.1148/radiol.211367
https://doi.org/10.1148/radiol.211367
Autor:
Xin-zhen Meng, Yun Liu, De-hua Tu, Xiang-hui Bai, Fang Lu, Hong-bo Xie, Wen-dang Song, Xin-zhong Zhang, Xiao-dong Zhou, Yu-fang Gao, Li-jie Wang, Xin-yang Sun, Wei-ji Su, Qi-jun Zhang, Feng-yan Tao, Ai-guo Ma, Ling-ming Kong, Li-yi Zhang, Yi-niu Wang
Publikováno v:
Journal of Health Psychology. 21:1383-1393
This study aimed to develop a Chinese Mental Resilience Scale. A total of 2500 healthy participants, in two representative samples of the Chinese population, were administered the scale. Exploratory factor analysis, confirmatory factor analysis, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::879c37590f8bcbf22747e17abfb8c966
https://doi.org/10.1177/1359105314554474
https://doi.org/10.1177/1359105314554474