Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Bozhi Liu"'
Publikováno v:
International Journal of Hyperthermia, Vol 37, Iss 1, Pp 651-659 (2020)
Purpose To determine the effectiveness of ablation for pulmonary metastases (PM) from hepatocellular carcinoma (HCC). Methods Between 2010 and 2017, the study analyzed 39 patients who had a median age of 59 years. Primary HCC was under control and th
Externí odkaz:
https://doaj.org/article/deb211cb0a08421fbd278aefeab9b6ec
Publikováno v:
Canadian Journal of Gastroenterology and Hepatology, Vol 2021 (2021)
Background. The presence of a capsule is an important prognostic factor in hepatocellular carcinoma (HCC). Capsule formation is affected by tumor-host interaction, which may include collagen deposition and extracellular matrix (ECM) degradation. Purp
Externí odkaz:
https://doaj.org/article/d4cf2c186cef4d5c87bd664b46d0275b
Autor:
Xiaoyan Ding, Wei Sun, Jinglong Chen, Wei Li, Yanjun Shen, Xiaodi Guo, Ying Teng, Xiaomin Liu, Shasha Sun, Jianying Wei, Wendong Li, Hui Chen, Bozhi Liu
Publikováno v:
Frontiers in Oncology, Vol 10 (2020)
BackgroundHepatocellular carcinoma (HCC) with portal vein tumor thrombosis (PVTT) portends a worse prognosis. The objective of this study was to compare the efficacy of percutaneous radiofrequency ablation (RFA) combined with transarterial chemoembol
Externí odkaz:
https://doaj.org/article/6c11ba036bfb46c286b917daec3b921d
Publikováno v:
IEEE Access, Vol 6, Pp 49779-49792 (2018)
We have developed a learning-based image transformation framework and successfully applied it to three common image transformation operations: downscaling, decolorization, and high dynamic range image tone mapping. We use a convolutional neural netwo
Externí odkaz:
https://doaj.org/article/f4206a6821cc43ad88bac8788f4704e1
Autor:
Jiasong Zhu, Qing Li, Rui Cao, Ke Sun, Tao Liu, Jonathan M. Garibaldi, Qingquan Li, Bozhi Liu, Guoping Qiu
Publikováno v:
Remote Sensing, Vol 11, Iss 1, p 73 (2019)
This paper presents a novel indoor topological localization method based on mobile phone videos. Conventional methods suffer from indoor dynamic environmental changes and scene ambiguity. The proposed Visual Landmark Sequence-based Indoor Localizatio
Externí odkaz:
https://doaj.org/article/7247f42ae17942a49f474d96dd3ea9ab
Publikováno v:
Remote Sensing, Vol 10, Iss 10, p 1553 (2018)
Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to
Externí odkaz:
https://doaj.org/article/200ceae4c482491b8a1f26a461bdc364
Publikováno v:
Remote Sensing, Vol 10, Iss 6, p 887 (2018)
Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest dee
Externí odkaz:
https://doaj.org/article/d1644d3ed1e247288beb01d01e125308
Publikováno v:
Journal of Hardware and Systems Security. 5:127-142
Embedded systems are widely deployed in life-critical systems, but system constraints often limit the depth of security used in these devices, potentially leaving them open to numerous threats. Side channel attacks (SCAs) are a popular attack to extr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5bbecc40e23396af95cf66a28ab9770
https://doi.org/10.1007/s41635-021-00117-1
https://doi.org/10.1007/s41635-021-00117-1
Autor:
Huang Min, Binbin Chen, Liu Bingping, Junyi Li, Lan Xuexin, Hailiang Wang, Xuanxian Cai, Shi Xiaoqi, Xiaoxiao Wu, Guozhao Chen, Jiaqian Wu, Bozhi Liu, Xu Xiai
Publikováno v:
SID Symposium Digest of Technical Papers. 52:1368-1371
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::18887d0c829f19c8bbd98bc48be8a542
https://doi.org/10.1002/sdtp.14959
https://doi.org/10.1002/sdtp.14959
Publikováno v:
IEEE Signal Processing Letters. 28:1749-1753
Recently, computational models based on deep neural networks have made impressive progress in predicting the visual saliency of human beings. Relying on the powerful capability of some pre-trained networks, various models can extract diverse deep fea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfe8011f03dae9d4f10e0327be807075
https://doi.org/10.1109/lsp.2021.3104757
https://doi.org/10.1109/lsp.2021.3104757