Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Suzhe WANG"'
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 54, Iss 5, Pp 789-795 (2023)
Purposes Deep compressed sensing is the use of deep learning to solve the problems existing in traditional compressed sensing, such as the adaptability of observation matrix to traditional signal compression and the dependency on dictionary by recons
Externí odkaz:
https://doaj.org/article/f035266d362a423aa8e73f1ebab08015
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
Vision transformer architectures attract widespread interest due to their robust representation capabilities of global features. Transformer-based methods as the encoder achieve superior performance compared to convolutional neural networks and other
Externí odkaz:
https://doaj.org/article/641dc5c71ddb44998cdcb84f81026002
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 53, Iss 6, Pp 1127-1133 (2022)
Stroke has a high disability rate and fatality rate. It is of great clinical significance to study the automatic recognition and segmentation of stroke lesions. Convolutional neural network can not make use of time sequence correlation of medical ima
Externí odkaz:
https://doaj.org/article/f6d838e9b55f4a05951c6e3acf85fbda
Publikováno v:
Computers, Materials & Continua; 2024, Vol. 81 Issue 1, p1177-1196, 20p
Publikováno v:
2022 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a068dc91749be12cbb4df278ce8ee6e6
https://doi.org/10.1109/icceai55464.2022.00032
https://doi.org/10.1109/icceai55464.2022.00032
Publikováno v:
Computers and Electrical Engineering. 103:108345
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7dff489d9a3658f42916b9542969609
https://doi.org/10.1016/j.compeleceng.2022.108345
https://doi.org/10.1016/j.compeleceng.2022.108345
Publikováno v:
Electronics; Volume 11; Issue 16; Pages: 2612
Deep learning-based techniques can obtain high precision for multimodal stroke segmentation tasks. However, the performance often requires a large number of training examples. Additionally, existing data extension approaches for the segmentation are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::694158e5a8a780c37d5fe036f052ea9b
https://doi.org/10.3390/electronics11162612
https://doi.org/10.3390/electronics11162612