Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Meihao Wang"'
Autor:
Xiao Chen, Yang Zhang, Jiahuan Zhou, Xiao Wang, Xinmiao Liu, Ke Nie, Xiaomin Lin, Wenwen He, Min-Ying Su, Guoquan Cao, Meihao Wang
Publikováno v:
Frontiers in Oncology, Vol 12 (2022)
PurposeTo implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.Materials and MethodsA total of 2
Externí odkaz:
https://doaj.org/article/0aa3dc1e0f3c4d2abbeb529469efc4d6
Autor:
Xiao Chen, Qingshan Deng, Qiang Wang, Xinmiao Liu, Lei Chen, Jinjin Liu, Shuangquan Li, Meihao Wang, Guoquan Cao
Publikováno v:
Frontiers in Public Health, Vol 10 (2022)
PurposeTo standardize the radiography imaging procedure, an image quality control framework using the deep learning technique was developed to segment and evaluate lumbar spine x-ray images according to a defined quality control standard.Materials an
Externí odkaz:
https://doaj.org/article/3b1b774b63c242a59fe6ec578a46c710
Autor:
Jiejie Zhou, Yan-Lin Liu, Yang Zhang, Jeon-Hor Chen, Freddie J. Combs, Ritesh Parajuli, Rita S. Mehta, Huiru Liu, Zhongwei Chen, Youfan Zhao, Zhifang Pan, Meihao Wang, Risheng Yu, Min-Ying Su
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
BackgroundA wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RA
Externí odkaz:
https://doaj.org/article/2f65c7c8b295401da0052da4b4502bc5
Autor:
Rita S. Mehta, Xinxin Wang, Siwa Chan, Jeon-Hor Chen, Min-Ying Su, Jiejie Zhou, Dah-Cherng Yeh, Peter Chang, Daniel S. Chow, Tiffany C. Kwong, Meihao Wang, Ritesh Parajuli, Yang Zhang, Yezhi Lin
Publikováno v:
Eur Radiol
European radiology, vol 31, iss 4
European radiology, vol 31, iss 4
To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5
Autor:
Kyoung Eun Lee, Meihao Wang, Jiejie Zhou, Yang Zhang, Min-Ying Su, Kai-Ting Chang, Yezhi Lin, Jiance Li, Daniel S. Chow, Zhifang Pan, Ouchen Wang, Peter Chang
Publikováno v:
J Magn Reson Imaging
Journal of magnetic resonance imaging : JMRI, vol 51, iss 3
Journal of magnetic resonance imaging : JMRI, vol 51, iss 3
Background Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported. Purpose To evaluate the diagnostic accuracy of mass lesions using region of i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ebf43ba292ffb6b1e55875f23d72a5d0
https://europepmc.org/articles/PMC7709823/
https://europepmc.org/articles/PMC7709823/