Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Dehui, Xiang"'
Publikováno v:
Clinical and Experimental Hypertension, Vol 45, Iss 1 (2023)
Objective To explore the association of renal surface nodularity (RSN) with the increased adverse vascular event (AVE) risk in patients with arterial hypertension. Methods This cross-sectional study included patients with arterial hypertension aged 1
Externí odkaz:
https://doaj.org/article/e356c91c14a64d21a0437902da7017cf
Publikováno v:
Frontiers in Neuroscience, Vol 16 (2022)
Externí odkaz:
https://doaj.org/article/a67c9415b0754c0398e2636611ef631b
Publikováno v:
International Journal of Hypertension, Vol 2022 (2022)
Background. This study sought to explore the association between quantitative classification of renal surface nodularity (qRSN) based on computed tomography (CT) imaging and early renal injury (ERI) in patients with arterial hypertension. Methods. A
Externí odkaz:
https://doaj.org/article/44a7082ef9184613a162a4bd07d568db
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 27:1237-1248
Lung tumor segmentation in PET-CT images plays an important role to assist physicians in clinical application to accurately diagnose and treat lung cancer. However, it is still a challenging task in medical image processing field. Due to respiration
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::98f20f978faa1e977698e36c5cdf7a6e
https://doi.org/10.1109/jbhi.2022.3186275
https://doi.org/10.1109/jbhi.2022.3186275
Autor:
Wenxuan, Xu, Yun, Bian, Yuxuan, Lu, Qingquan, Meng, Weifang, Zhu, Fei, Shi, Xinjian, Chen, Chengwei, Shao, Dehui, Xiang
Publikováno v:
Medical Physics. 50:1586-1600
Medical image segmentation is an important task in the diagnosis and treatment of cancers. The low contrast and highly flexible anatomical structure make it challenging to accurately segment the organs or lesions.To improve the segmentation accuracy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb7ad27146180a7a690f5185f52e346b
https://doi.org/10.1002/mp.16072
https://doi.org/10.1002/mp.16072
Autor:
Pengfei Zhang, Xinjian Chen, Ziting Yin, Xin Zhou, Qingxin Jiang, Weifang Zhu, Dehui Xiang, Yun Tang, Fei Shi
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. :1-11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2d8d3b685440ccfc0c32fae7ddeb927
https://doi.org/10.1109/jbhi.2023.3270711
https://doi.org/10.1109/jbhi.2023.3270711
Autor:
Meng Wang, Tian Lin, Yuanyuan Peng, Weifang Zhu, Yi Zhou, Fei Shi, Kai Yu, Qingquan Meng, Yong Liu, Zhongyue Chen, Yuhe Shen, Dehui Xiang, Haoyu Chen, Xinjian Chen
Publikováno v:
IEEE Transactions on Biomedical Engineering. :1-12
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6baedd2750022d07951605468994237b
https://doi.org/10.1109/tbme.2023.3234031
https://doi.org/10.1109/tbme.2023.3234031
Autor:
Jiahuan Song, Xinjian Chen, Qianlong Zhu, Fei Shi, Dehui Xiang, Zhongyue Chen, Ying Fan, Lingjiao Pan, Weifang Zhu
Publikováno v:
IEEE Transactions on Medical Imaging. 41:2273-2284
Learning how to capture long-range dependencies and restore spatial information of down-sampled feature maps are the basis of the encoder-decoder structure networks in medical image segmentation. U-Net based methods use feature fusion to alleviate th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68d0d39b7c9aebf7f54e3fd838832e47
https://doi.org/10.1109/tmi.2022.3162111
https://doi.org/10.1109/tmi.2022.3162111
Publikováno v:
Medical Imaging 2023: Digital and Computational Pathology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e19a60e1553dc40682aa6d4335b21afc
https://doi.org/10.1117/12.2651394
https://doi.org/10.1117/12.2651394
Publikováno v:
Medical Imaging 2023: Digital and Computational Pathology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7cb7f9240b5df873d61ee522b85e162
https://doi.org/10.1117/12.2651219
https://doi.org/10.1117/12.2651219