Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Gengyan Zhao"'
Autor:
Badhan Kumar Das, Gengyan Zhao, Saahil Islam, Thomas J. Re, Dorin Comaniciu, Eli Gibson, Andreas Maier
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-9 (2024)
Abstract Vision transformers (ViTs) have revolutionized computer vision by employing self-attention instead of convolutional neural networks and demonstrated success due to their ability to capture global dependencies and remove spatial biases of loc
Externí odkaz:
https://doaj.org/article/1c39c5db40d64139a482431b67688bd1
Autor:
Yilin Liu, Brendon M. Nacewicz, Gengyan Zhao, Nagesh Adluru, Gregory R. Kirk, Peter A. Ferrazzano, Martin A. Styner, Andrew L. Alexander
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most existing deep learning based approaches in neuroimag
Externí odkaz:
https://doaj.org/article/b5a9e85a8363455a8a42b1dd3dfb0d30
Autor:
Gyujoon Hwang, Bruce Hermann, Veena A. Nair, Lisa L. Conant, Kevin Dabbs, Jed Mathis, Cole J. Cook, Charlene N. Rivera-Bonet, Rosaleena Mohanty, Gengyan Zhao, Dace N. Almane, Andrew Nencka, Elizabeth Felton, Aaron F. Struck, Rasmus Birn, Rama Maganti, Colin J. Humphries, Manoj Raghavan, Edgar A. DeYoe, Barbara B. Bendlin, Vivek Prabhakaran, Jeffrey R. Binder, Mary E. Meyerand
Publikováno v:
NeuroImage: Clinical, Vol 25, Iss , Pp - (2020)
The association of epilepsy with structural brain changes and cognitive abnormalities in midlife has raised concern regarding the possibility of future accelerated brain and cognitive aging and increased risk of later life neurocognitive disorders. T
Externí odkaz:
https://doaj.org/article/244c02cc38af4acd98396eed3760bbd5
Publikováno v:
EJNMMI Physics, Vol 5, Iss 1, Pp 1-15 (2018)
Abstract Background To develop and evaluate the feasibility of a data-driven deep learning approach (deepAC) for positron-emission tomography (PET) image attenuation correction without anatomical imaging. A PET attenuation correction pipeline was dev
Externí odkaz:
https://doaj.org/article/155c3fa7ba2f4e15bb83761480ead468
Autor:
Youngjin Yoo, Gengyan Zhao, Andreea E. Sandu, Thomas J. Re, Jyotipriya Das, Hesheng Wang, Michelle Kim, Colette Shen, Yueh Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, Dorin Comaniciu
Publikováno v:
Medical Imaging 2023: Computer-Aided Diagnosis.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d1dac397001f311356c48161a63e0245
https://doi.org/10.1117/12.2653909
https://doi.org/10.1117/12.2653909
Autor:
Gengyan Zhao, Youngjin Yoo, Thomas J. Re, Jyotipriya Das, Hesheng Wang, Michelle Kim, Colette Shen, Yueh Z. Lee, Douglas Kondziolka, Mohannad Ibrahim, Jun Lian, Rajan Jain, Tong Zhu, Hemant Parmar, James M. Balter, Yue Cao, Eli Gibson, Dorin Comaniciu
Publikováno v:
Medical Imaging 2023: Image Processing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cf478161efc9375ba7ac0d78f4b9181
https://doi.org/10.1117/12.2654281
https://doi.org/10.1117/12.2654281
Autor:
Cole J. Cook, Elizabeth Meyerand, Andrew S. Nencka, Aaron F. Struck, Edgar A. DeYoe, Megan Rozman, Gyujoon Hwang, Jed Mathis, Rosaleena Mohanty, Vivek Prabhakaran, Gengyan Zhao, Jeffrey R. Binder, Linda Allen, Rama Maganti, Charlene N. Rivera-Bonet, Veena A. Nair, Elizabeth A. Felton, Neelima Tellapragada, Manoj Raghavan, Dace Almane, Candida Ustine, Lisa L. Conant, Onyekachi O. Nwoke, Rasmus M. Birn, Courtney Forseth, Peter Kraegel, Colin Humphries, Bruce P. Hermann
Publikováno v:
Brain Connectivity. 9:184-193
The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7786cc94339ed5d3264495272b2d32b4
https://doi.org/10.1089/brain.2018.0601
https://doi.org/10.1089/brain.2018.0601
Publikováno v:
NeuroImage. 175:32-44
Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3a4ded91d74d7661c8554d3a142d72d4
https://doi.org/10.1016/j.neuroimage.2018.03.065
https://doi.org/10.1016/j.neuroimage.2018.03.065
Publikováno v:
Magnetic Resonance in Medicine. 80:2759-2770
Purpose To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e74ed069e1428495b4ae2f884b5d45d5
https://doi.org/10.1002/mrm.27229
https://doi.org/10.1002/mrm.27229
Publikováno v:
Magnetic Resonance in Medicine. 79:2379-2391
Purpose To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9d26e14be11f7028a0a4888f6ebf66f6
https://doi.org/10.1002/mrm.26841
https://doi.org/10.1002/mrm.26841