Zobrazeno 1 - 10
of 555
pro vyhledávání: '"Lu Wenqi"'
Autor:
LU Wenqi, WAN Lin
Publikováno v:
Jichu yixue yu linchuang, Vol 43, Iss 11, Pp 1728-1732 (2023)
Graft-versus-host disease (GVHD) is one of the most serious complications after allogeneic hematopoietic stem cell transplantation (allo-HSCT), resulting a very high mortality risk and no effective diagnosis and treatment method. Although an increasi
Externí odkaz:
https://doaj.org/article/2bda9fe29592434aabf1ccca3f896ca8
Autor:
Yang Hao, Jia Hao, Zhu Zuchao, Su Xianghui, Lu Wenqi, Maciej Gruszczynski, Ding Qiangmin, Gao Panlong
Publikováno v:
Frontiers in Energy Research, Vol 10 (2022)
In recent years, much attention has been paid to the application of high-speed centrifugal pumps; still, the development of this pump faces several challenges. In order to obtain a more comprehensive understanding of the high-speed centrifugal pump,
Externí odkaz:
https://doaj.org/article/8b001205ecc846b786513e5337c629dc
Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. However, due to the casc
Externí odkaz:
http://arxiv.org/abs/2407.13426
Autor:
Zhang, Yuting, Liu, Boyang, Bunting, Karina V., Brind, David, Thorley, Alexander, Karwath, Andreas, Lu, Wenqi, Zhou, Diwei, Wang, Xiaoxia, Mobley, Alastair R., Tica, Otilia, Gkoutos, Georgios, Kotecha, Dipak, Duan, Jinming
The echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF). In order to quantify LVEF automatically and accurately, this paper proposes a new p
Externí odkaz:
http://arxiv.org/abs/2403.12152
In unsupervised medical image registration, the predominant approaches involve the utilization of a encoder-decoder network architecture, allowing for precise prediction of dense, full-resolution displacement fields from given paired images. Despite
Externí odkaz:
http://arxiv.org/abs/2402.03585
The Alternating Direction Method of Multipliers (ADMM) has gained significant attention across a broad spectrum of machine learning applications. Incorporating the over-relaxation technique shows potential for enhancing the convergence rate of ADMM.
Externí odkaz:
http://arxiv.org/abs/2401.00657
U-Net style networks are commonly utilized in unsupervised image registration to predict dense displacement fields, which for high-resolution volumetric image data is a resource-intensive and time-consuming task. To tackle this challenge, we first pr
Externí odkaz:
http://arxiv.org/abs/2307.02997
Autor:
Jia, Xi, Bartlett, Joseph, Chen, Wei, Song, Siyang, Zhang, Tianyang, Cheng, Xinxing, Lu, Wenqi, Qiu, Zhaowen, Duan, Jinming
Unsupervised image registration commonly adopts U-Net style networks to predict dense displacement fields in the full-resolution spatial domain. For high-resolution volumetric image data, this process is however resource-intensive and time-consuming.
Externí odkaz:
http://arxiv.org/abs/2211.16342
Due to their extreme long-range modeling capability, vision transformer-based networks have become increasingly popular in deformable image registration. We believe, however, that the receptive field of a 5-layer convolutional U-Net is sufficient to
Externí odkaz:
http://arxiv.org/abs/2208.04939
Autor:
Wang, Kun, Yu, Dong, Liang, Xinyi, Lu, Wenqi, Jiang, Xuan, Tashpulatov, Khurshid, Zeng, Jingbin, Wen, Cong-Ying
Publikováno v:
In Microchemical Journal September 2024 204