Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Xipeng PAN"'
Autor:
Xipeng Pan, Siyang Feng, Yumeng Wang, Jiale Chen, Huan Lin, Zimin Wang, Feihu Hou, Cheng Lu, Xin Chen, Zhenbing Liu, Zhenhui Li, Yanfen Cui, Zaiyi Liu
Publikováno v:
Heliyon, Vol 10, Iss 10, Pp e30779- (2024)
Background and objective: Spatial interaction between tumor-infiltrating lymphocytes (TILs) and tumor cells is valuable in predicting the effectiveness of immune response and prognosis amongst patients with lung adenocarcinoma (LUAD). Recent evidence
Externí odkaz:
https://doaj.org/article/7a78a433db1c48599c8670ff19546266
Publikováno v:
Cognitive Robotics, Vol 4, Iss , Pp 74-87 (2024)
Driving safety is significant to building a people-oriented and harmonious society, Tires are one of the key components of a vehicle and the character information on the tire sidewall is critical to their storage and usage. However, due to the divers
Externí odkaz:
https://doaj.org/article/ac11d1bcc1e043b98a1a6fc3ee64e550
Autor:
Yumeng Wang, Xipeng Pan, Huan Lin, Chu Han, Yajun An, Bingjiang Qiu, Zhengyun Feng, Xiaomei Huang, Zeyan Xu, Zhenwei Shi, Xin Chen, Bingbing Li, Lixu Yan, Cheng Lu, Zhenhui Li, Yanfen Cui, Zaiyi Liu, Zhenbing Liu
Publikováno v:
Journal of Translational Medicine, Vol 20, Iss 1, Pp 1-17 (2022)
Abstract Background Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However,
Externí odkaz:
https://doaj.org/article/69c7dfac3f984bfe881164568865dc3b
Autor:
Yumeng Wang, Huan Lin, Ningning Yao, Xiaobo Chen, Bingjiang Qiu, Yanfen Cui, Yu Liu, Bingbing Li, Chu Han, Zhenhui Li, Wei Zhao, Zimin Wang, Xipeng Pan, Cheng Lu, Jun Liu, Zhenbing Liu, Zaiyi Liu
Publikováno v:
iScience, Vol 26, Iss 9, Pp 107635- (2023)
Summary: The increased amount of tertiary lymphoid structures (TLSs) is associated with a favorable prognosis in patients with lung adenocarcinoma (LUAD). However, evaluating TLSs manually is an experience-dependent and time-consuming process, which
Externí odkaz:
https://doaj.org/article/6c3f4cc6df9c480497b066b5d8d077a1
Autor:
Huan Lin, Xipeng Pan, Zhengyun Feng, Lixu Yan, Junjie Hua, Yanting Liang, Chu Han, Zeyan Xu, Yumeng Wang, Lin Wu, Yanfen Cui, Xiaomei Huang, Zhenwei Shi, Xin Chen, Xiaobo Chen, Qingling Zhang, Changhong Liang, Ke Zhao, Zhenhui Li, Zaiyi Liu
Publikováno v:
Journal of Translational Medicine, Vol 20, Iss 1, Pp 1-13 (2022)
Abstract Background High immune infiltration is associated with favourable prognosis in patients with non-small-cell lung cancer (NSCLC), but an automated workflow for characterizing immune infiltration, with high validity and reliability, remains to
Externí odkaz:
https://doaj.org/article/c0cdbc87795d432abc70faea98d9c98f
Publikováno v:
Cognitive Robotics, Vol 2, Iss , Pp 155-163 (2022)
Traditional digital image processing methods extract disease features manually, which have low efficiency and low recognition accuracy. To solve this problem, In this paper, we propose a convolutional neural network architecture FL-EfficientNet (Foca
Externí odkaz:
https://doaj.org/article/3875911e1321480aba48d748b86f9219
Autor:
Peixin Qu, Tengfei Li, Guohou Li, Zhen Tian, Xiwang Xie, Wenyi Zhao, Xipeng Pan, Weidong Zhang
Publikováno v:
Cognitive Robotics, Vol 2, Iss , Pp 211-221 (2022)
Underwater images are serious problems affected by the absorption and scattering of light. At present, the existing sharpening methods can't effectively solve all underwater image degradation problems, thus it is necessary to propose a specific solut
Externí odkaz:
https://doaj.org/article/086012fde281425bae60333ec4cf2e11
Autor:
Xipeng Pan, Huan Lin, Chu Han, Zhengyun Feng, Yumeng Wang, Jiatai Lin, Bingjiang Qiu, Lixu Yan, Bingbing Li, Zeyan Xu, Zhizhen Wang, Ke Zhao, Zhenbing Liu, Changhong Liang, Xin Chen, Zhenhui Li, Yanfen Cui, Cheng Lu, Zaiyi Liu
Publikováno v:
iScience, Vol 25, Iss 12, Pp 105605- (2022)
Summary: A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for
Externí odkaz:
https://doaj.org/article/a1af3ef1e5064736b8f016797a9708d0
Publikováno v:
Sensors, Vol 23, Iss 16, p 7205 (2023)
Infrared and visible image fusion aims to generate a single fused image that not only contains rich texture details and salient objects, but also facilitates downstream tasks. However, existing works mainly focus on learning different modality-specif
Externí odkaz:
https://doaj.org/article/6384e17e3fbb4be5bb92ef47bf1462ad
Autor:
Zeyan Xu, Yong Li, Yingyi Wang, Shenyan Zhang, Yanqi Huang, Su Yao, Chu Han, Xipeng Pan, Zhenwei Shi, Yun Mao, Yao Xu, Xiaomei Huang, Huan Lin, Xin Chen, Changhong Liang, Zhenhui Li, Ke Zhao, Qingling Zhang, Zaiyi Liu
Publikováno v:
Cancer Cell International, Vol 21, Iss 1, Pp 1-12 (2021)
Abstract Background Profound heterogeneity in prognosis has been observed in colorectal cancer (CRC) patients with intermediate levels of disease (stage II–III), advocating the identification of valuable biomarkers that could improve the prognostic
Externí odkaz:
https://doaj.org/article/76a0ea91fec74064bf7e9d04a21ad289