Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Linyang He"'
Autor:
Dengfa Yang, Yang Yang, MinYi Zhao, Hongli Ji, Zhongfeng Niu, Bo Hong, Hengfeng Shi, Linyang He, Meihua Shao, Jian Wang
Publikováno v:
BMC Cancer, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Objective To intelligently evaluate the invasiveness of pure ground-glass nodules with multiple classifications using deep learning. Methods pGGNs in 1136 patients were pathologically confirmed as lung precursor lesions [atypical adenomatous
Externí odkaz:
https://doaj.org/article/88a2dea4819b4303bfbb05790d46d2f2
Publikováno v:
BMC Infectious Diseases, Vol 24, Iss 1, Pp 1-11 (2024)
Abstract Background and purpose The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of
Externí odkaz:
https://doaj.org/article/188a68301d8c493184385cbb4e708093
Publikováno v:
BMC Musculoskeletal Disorders, Vol 23, Iss 1, Pp 1-12 (2022)
Abstract Background The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are fe
Externí odkaz:
https://doaj.org/article/24e67173b42645ce9372f0f145147f17
Autor:
Hengfeng Shi, Zhihua Xu, Guohua Cheng, Hongli Ji, Linyang He, Juan Zhu, Hanjin Hu, Zongyu Xie, Weiqun Ao, Jian Wang
Publikováno v:
European Journal of Medical Research, Vol 27, Iss 1, Pp 1-12 (2022)
Abstract Background The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severity of COVID-19 determines the management, treatment, and even prognosis. We aim to develop and validate a radiomics nomogram for identifying patients with se
Externí odkaz:
https://doaj.org/article/03e8df0fe4114310aa2d50250cb86458
Publikováno v:
IEEE Access, Vol 9, Pp 33583-33594 (2021)
The airway tree is one of the most important part in human respiratory system. Airway segmentation plays a crucial role in pulmonary disease diagnosis, localization and surgical navigation. We propose a novel method to improve airway segmentation in
Externí odkaz:
https://doaj.org/article/980965c5dba64513973d725afe224108
Autor:
Meihua Shao, Zhongfeng Niu, Linyang He, Zhaoxing Fang, Jie He, Zongyu Xie, Guohua Cheng, Jian Wang
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
We aimed to build radiomics models based on triple-phase CT images combining clinical features to predict the risk rating of gastrointestinal stromal tumors (GISTs). A total of 231 patients with pathologically diagnosed GISTs from July 2012 to July 2
Externí odkaz:
https://doaj.org/article/271329424a3d4680b6358bc17f086af0
Publikováno v:
Frontiers in Oncology, Vol 11 (2021)
ObjectSTAS is associated with poor differentiation, KRAS mutation and poor recurrence-free survival. The aims of this study are to evaluate the ability of intra- and perinodular radiomic features to distinguish STAS at non-contrast CT.Patients and Me
Externí odkaz:
https://doaj.org/article/280fc38649e74ba29476d1ceabb40592
Publikováno v:
Applied Intelligence. 53:11357-11372
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1183c75b483f56b5d729502484d64a76
https://doi.org/10.1007/s10489-022-04075-1
https://doi.org/10.1007/s10489-022-04075-1
Publikováno v:
Multimedia Tools and Applications.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e22a0fb55d948dd1d2f66cf787edeea8
https://doi.org/10.1007/s11042-023-15299-9
https://doi.org/10.1007/s11042-023-15299-9
Development of automatic measurement for patellar height based on deep learning and knee radiographs
Publikováno v:
European Radiology. 30:4974-4984
To develop and evaluate the performance of a deep learning–based system for automatic patellar height measurements using knee radiographs. The deep learning–based algorithm was developed with a data set consisting of 1018 left knee radiographs fo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7051bff1c1f34cb4c84e55ddb736465
https://doi.org/10.1007/s00330-020-06856-z
https://doi.org/10.1007/s00330-020-06856-z