Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Deng Mo"'
Publikováno v:
口腔疾病防治, Vol 24, Iss 2, Pp 74-78 (2016)
Objective To observe the clinical efficacy of multipoint radiofrequency thermal coagulation on trigeminal neuralgia management. Methods 180 patients diagnosed as primary trigeminal neuralgia were included in this study. The patients were randomly div
Externí odkaz:
https://doaj.org/article/bc5124762016456ca240d47940ce154b
Autor:
Ge, Baoliang, He, Yanping, Deng, Mo, Rahman, Md Habibur, Wang, Yijin, Wu, Ziling, Wong, Chung Hong N., Chan, Michael K., Ho, Yi-Ping, Duan, Liting, Yaqoob, Zahid, So, Peter T. C., Barbastathis, George, Zhou, Renjie
Three-dimensional (3D) image cytometers may significantly improve the cell analysis accuracy to facilitate biological discoveries and clinical diagnosis, but their development is curbed by the low imaging throughput. Here we report SIngle-frame LAbel
Externí odkaz:
http://arxiv.org/abs/2202.03627
Autor:
Mudavadkar, Govind Rajesh1 (AUTHOR) mudavadkar.g@northeastern.edu, Deng, Mo1 (AUTHOR) m.deng@northeastern.edu, Al-Heejawi, Salah Mohammed Awad1 (AUTHOR) s.al-heejawi@northeastern.edu, Arora, Isha Hemant2 (AUTHOR) arora.isha@northeastern.edu, Breggia, Anne3 (AUTHOR), Ahmad, Bilal4 (AUTHOR) bilal.ahmad@spectrumhcp.com, Christman, Robert4 (AUTHOR) robert.christman@spectrumhcp.com, Ryan, Stephen T.4 (AUTHOR) stephen.ryan@mainehealth.org, Amal, Saeed5 (AUTHOR) s.amal@northeastern.edu
Publikováno v:
Diagnostics (2075-4418). Aug2024, Vol. 14 Issue 16, p1746. 17p.
Deep learning (DL) has been applied extensively in many computational imaging problems, often leading to superior performance over traditional iterative approaches. However, two important questions remain largely unanswered: first, how well can the t
Externí odkaz:
http://arxiv.org/abs/2004.06355
The quality of inverse problem solutions obtained through deep learning [Barbastathis et al, 2019] is limited by the nature of the priors learned from examples presented during the training phase. In the case of quantitative phase retrieval [Sinha et
Externí odkaz:
http://arxiv.org/abs/1907.11713
Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been shown to outperform classical optimization techniques in several computational imaging problems. DNNs are trained by solving an optimization problem implies the ch
Externí odkaz:
http://arxiv.org/abs/1906.05687
Publikováno v:
In Journal of Natural Gas Geoscience June 2023 8(3):169-185
Deep Neural Network (DNN)-based image reconstruction, despite many successes, often exhibits uneven fidelity between high and low spatial frequency bands. In this paper we propose the Learning Synthesis by DNN (LS-DNN) approach where two DNNs process
Externí odkaz:
http://arxiv.org/abs/1811.07945
Autor:
Deng, Mo1 (AUTHOR), Zhao, Guowei1 (AUTHOR), Lin, Xiaobing2 (AUTHOR) linxiaobing07@cdut.edu.cn, Chen, Chunyu2 (AUTHOR), Li, Longlong1 (AUTHOR), Liang, Qingshao2 (AUTHOR) linxiaobing07@cdut.edu.cn
Publikováno v:
Minerals (2075-163X). Nov2023, Vol. 13 Issue 11, p1406. 19p.
Computational imaging through scatter generally is accomplished by first characterizing the scattering medium so that its forward operator is obtained; and then imposing additional priors in the form of regularizers on the reconstruction functional s
Externí odkaz:
http://arxiv.org/abs/1711.06810