Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Runnan Chen"'
Autor:
GuoYong Lin, ZhiSen Gao, Shun Wu, JianPing Zheng, XiangQiong Guo, XiaoHong Zheng, RunNan Chen
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)
Abstract Lung adenocarcinoma (LUAD) is one of the sole causes of death in lung cancer patients. This study combined with single-cell RNA-seq analysis to identify tumor stem-related prognostic models to predict the prognosis of lung adenocarcinoma, ch
Externí odkaz:
https://doaj.org/article/e92bf2de5ae3480b8943c3a71d62b002
Autor:
Runnan Chen
Publikováno v:
Highlights in Science, Engineering and Technology. 29:44-50
The new energy production and consumption revolution have made a huge leap since humans entered the 21st century. Generally, researchers can categorize energy twofold: traditional and green resources. Also, a handy work for governors nowadays is eval
Publikováno v:
The International Journal of Biochemistry & Cell Biology. 153:106313
Acute respiratory distress syndrome (ARDS) is a common and serious respiratory illness with substantial morbidity and mortality. Circular RNAs have been demonstrated to participate in various diseases processes. However, the biological function and m
Autor:
Xing Sun, Penghao Zhou, Guanyu Cai, Jian Wu, Pai Peng, Wenzhe Wang, Xiaowei Guo, Mengdan Zhang, Runnan Chen
Publikováno v:
IJCAI
Multi-modal cues presented in videos are usually beneficial for the challenging video-text retrieval task on internet-scale datasets. Recent video retrieval methods take advantage of multi-modal cues by aggregating them to holistic high-level semanti
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis. However, the current methods are time-consuming and suffer from large biases in landmark
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1de7ecd9ccc62e955fec869d9f87879d
http://arxiv.org/abs/2107.09899
http://arxiv.org/abs/2107.09899
Autor:
Zhiwen Lin, Xingjia Pan, Runnan Chen, Ren Yuqiang, Feiyue Huang, Haolei Yuan, Lei Yang, Xiaowei Guo, Nenglun Chen, Wenping Wang
Publikováno v:
ACM Multimedia
We study the problem of weakly supervised grounded image captioning. That is, given an image, the goal is to automatically generate a sentence describing the context of the image with each noun word grounded to the corresponding region in the image.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d2ec53bc176c91d676106a70368c9bad
Well-annotated medical images are costly and sometimes even impossible to acquire, hindering landmark detection accuracy to some extent. Semi-supervised learning alleviates the reliance on large-scale annotated data by exploiting the unlabeled data t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7c13d6d60f7348fd2e0fa9f650b7360c
Publikováno v:
CVPR
Learning structures of 3D shapes is a fundamental problem in the field of computer graphics and geometry processing. We present a simple yet interpretable unsupervised method for learning a new structural representation in the form of 3D structure po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ca34572edae72ff9f593cfc37fa6de4
http://arxiv.org/abs/2003.01661
http://arxiv.org/abs/2003.01661
Publikováno v:
Computer Vision – ECCV 2020 ISBN: 9783030585549
ECCV (15)
ECCV (15)
Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics. Existing practice for specifying the target tooth arrangement involves tedious manual operations with the outcome quality depending heavily on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c8ad4e8c365371dd0ffac44f72f56f9
https://doi.org/10.1007/978-3-030-58555-6_29
https://doi.org/10.1007/978-3-030-58555-6_29
Autor:
Runnan Chen, Changjian Li, Yuanfeng Zhou, Wenping Wang, Zhiming Cui, Dinggang Shen, Guodong Wei, Nenglun Chen
Publikováno v:
Medical Image Analysis. 69:101949
Automatic and accurate segmentation of dental models is a fundamental task in computer-aided dentistry. Previous methods can achieve satisfactory segmentation results on normal dental models; however, they fail to robustly handle challenging clinical