Zobrazeno 1 - 10
of 1 650
pro vyhledávání: '"Guo, Pengfei"'
Autor:
He, Yufan, Guo, Pengfei, Tang, Yucheng, Myronenko, Andriy, Nath, Vishwesh, Xu, Ziyue, Yang, Dong, Zhao, Can, Simon, Benjamin, Belue, Mason, Harmon, Stephanie, Turkbey, Baris, Xu, Daguang, Li, Wenqi
Segmentation foundation models have attracted great interest, however, none of them are adequate enough for the use cases in 3D computed tomography scans (CT) images. Existing works finetune on medical images with 2D foundation models trained on natu
Externí odkaz:
http://arxiv.org/abs/2406.05285
In this paper, we give a approximation characterization, the lifting property, embedding properties and the duality of matrix weighted modulation spaces.
Externí odkaz:
http://arxiv.org/abs/2405.15171
Autor:
Pan, Junyao, Guo, Pengfei
In 2019, B\'ona and Smith introduced the notion of \emph{strong pattern avoidance}, that is, a permutation and its square both avoid a given pattern. In this paper, we enumerate the set of permutations $\pi$ which not only strongly avoid the pattern
Externí odkaz:
http://arxiv.org/abs/2404.01597
Autor:
Guo, Pengfei, Morningstar, Warren Richard, Vemulapalli, Raviteja, Singhal, Karan, Patel, Vishal M., Mansfield, Philip Andrew
Large machine learning models trained on diverse data have recently seen unprecedented success. Federated learning enables training on private data that may otherwise be inaccessible, such as domain-specific datasets decentralized across many clients
Externí odkaz:
http://arxiv.org/abs/2309.05213
Autor:
Pan, Junyao, Guo, Pengfei
A set of permutations is called sign-balanced if the set contains the same number of even permutations as odd permutations. Let $S_n(\sigma_1, \sigma_2, \ldots, \sigma_r)$ be the set of permutations in the symmetric group $S_n$ which avoids patterns
Externí odkaz:
http://arxiv.org/abs/2306.00033
Autor:
Shen, Yiqing, Guo, Pengfei, Wu, Jingpu, Huang, Qianqi, Le, Nhat, Zhou, Jinyuan, Jiang, Shanshan, Unberath, Mathias
The synergy of long-range dependencies from transformers and local representations of image content from convolutional neural networks (CNNs) has led to advanced architectures and increased performance for various medical image analysis tasks due to
Externí odkaz:
http://arxiv.org/abs/2303.15553
Autor:
Zhao, Wenhao1 (AUTHOR), Guo, Pengfei1,2 (AUTHOR) guopengfei@nwpu.edu.cn, Wu, Jiahao1 (AUTHOR), Lin, Deyou1 (AUTHOR), Jia, Ning1 (AUTHOR), Fang, Zhiyu1 (AUTHOR), Liu, Chong1 (AUTHOR), Ye, Qian1 (AUTHOR), Zou, Jijun3 (AUTHOR), Zhou, Yuanyuan4 (AUTHOR), Wang, Hongqiang1,2 (AUTHOR) hongqiang.wang@nwpu.edu.cn
Publikováno v:
Nano-Micro Letters. 5/3/2024, Vol. 16 Issue 1, p1-14. 14p.
Autor:
Hou, Xirui, Guo, Pengfei, Wang, Puyang, Liu, Peiying, Lin, Doris D. M., Fan, Hongli, Li, Yang, Wei, Zhiliang, Lin, Zixuan, Jiang, Dengrong, Jin, Jin, Kelly, Catherine, Pillai, Jay J., Huang, Judy, Pinho, Marco C., Thomas, Binu P., Welch, Babu G., Park, Denise C., Patel, Vishal M., Hillis, Argye E., Lu, Hanzhang
Publikováno v:
npj Digital Medicine (2023) 116
Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack
Externí odkaz:
http://arxiv.org/abs/2204.11669
In Heterogeneous Face Recognition (HFR), the objective is to match faces across two different domains such as visible and thermal. Large domain discrepancy makes HFR a difficult problem. Recent methods attempting to fill the gap via synthesis have ac
Externí odkaz:
http://arxiv.org/abs/2203.16669