Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Jie-zhi Cheng"'
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
Externí odkaz:
https://doaj.org/article/655e0e5163fd46d99f235db101c29529
Publikováno v:
BMC Musculoskeletal Disorders, Vol 23, Iss 1, Pp 1-11 (2022)
Abstract Background A deep convolutional neural network (DCNN) system is proposed to measure the lower limb parameters of the mechanical lateral distal femur angle (mLDFA), medial proximal tibial angle (MPTA), lateral distal tibial angle (LDTA), join
Externí odkaz:
https://doaj.org/article/eff9f5487d4c4336a7e7a8707ea12085
Autor:
Kun Sun, Zhicheng Jiao, Hong Zhu, Weimin Chai, Xu Yan, Caixia Fu, Jie-Zhi Cheng, Fuhua Yan, Dinggang Shen
Publikováno v:
Journal of Translational Medicine, Vol 19, Iss 1, Pp 1-10 (2021)
Abstract Background This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of b
Externí odkaz:
https://doaj.org/article/81a8324a4b614d6eb6ea2be38f627b22
Autor:
Qingfeng Wang, Qiyu Liu, Guoting Luo, Zhiqin Liu, Jun Huang, Yuwei Zhou, Ying Zhou, Weiyun Xu, Jie-Zhi Cheng
Publikováno v:
BMC Medical Informatics and Decision Making, Vol 20, Iss S14, Pp 1-12 (2020)
Abstract Background Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiogr
Externí odkaz:
https://doaj.org/article/8e4655e4508d40dd84a57ed9997b347c
Autor:
Qingfeng Wang, Xuehai Zhou, Chao Wang, Zhiqin Liu, Jun Huang, Ying Zhou, Changlong Li, Hang Zhuang, Jie-Zhi Cheng
Publikováno v:
IEEE Access, Vol 7, Pp 18450-18463 (2019)
Data imbalance issue generally exists in most medical image analysis problems and maybe getting important with the popularization of data-hungry deep learning paradigms. We explore the cutting-edge Wasserstein generative adversarial networks (WGANs)
Externí odkaz:
https://doaj.org/article/8f180955e7ab4566957efa120370acbe
Autor:
Jie-Zhi Cheng, 鄭介誌
101
As a potential biomarker for women''s cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide more objective measurement for vascular st
As a potential biomarker for women''s cardiovascular and chronic kidney diseases, breast arterial calcification (BAC) in mammography has become an emerging research topic in recent years. To provide more objective measurement for vascular st
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/46190175822037131812
Autor:
Jie-Zhi Cheng, 鄭介誌
95
Boundary information of the object of interest in sonography is the fundamental basis for many clinical studies. It can help to manifest the abnormality of anatomy by characterizing the morphological features and plays the essential role in n
Boundary information of the object of interest in sonography is the fundamental basis for many clinical studies. It can help to manifest the abnormality of anatomy by characterizing the morphological features and plays the essential role in n
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/74833813995255310003
Publikováno v:
Computational Intelligence and Neuroscience. 2022:1-13
Lung cancer accounts for the greatest number of cancer-related mortality, while the accurate evaluation of pulmonary nodules in computed tomography (CT) images can significantly increase the 5-year relative survival rate. Despite deep learning method
Autor:
Srikrishna Karanam, Jiayu Huo, Terrence Chen, Ziyan Wu, Xi Ouyang, Jie-Zhi Cheng, Xiang Sean Zhou, Qian Wang
Publikováno v:
IEEE Transactions on Medical Imaging. 40:2698-2710
We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are not fully addressed, limiting its broader usage. While recent methods
Autor:
Jinrong Yang, Xiang Li, Jie-Zhi Cheng, Zhong Xue, Feng Shi, Yuqing Ji, Xuechun Wang, Fan Yang
Publikováno v:
Computers in Biology and Medicine. 160:107002