no code implementations • 27 Mar 2024 • Yizhang Xia, Shihao Song, Zhanglu Hou, Junwen Xu, Juan Zou, YuAn Liu, Shengxiang Yang
To automatically adapt to various datasets, the ENAS framework is designed to automatically search a MHGR network with appropriate fusion positions and ratios.
no code implementations • 11 Mar 2024 • Juan Zou, Han Chu, Yizhang Xia, Junwen Xu, YuAn Liu, Zhanglu Hou
Specifically, the global search space requires a significant amount of computational resources and time, the scalable search space sacrifices the diversity of network structures and the hierarchical search space increases the search cost in exchange for network diversity.
no code implementations • 5 Mar 2024 • Juan Zou, Weiwei Jiang, Yizhang Xia, YuAn Liu, Zhanglu Hou
The process begins from a shallow network, grows and evolves, and gradually deepens into a complete network, reducing the search complexity in the global space.
no code implementations • 3 Jan 2024 • Wenxin Fan, Jian Cheng, Cheng Li, Xinrui Ma, Jing Yang, Juan Zou, Ruoyou Wu, Qiegen Liu, Shanshan Wang
Deep learning has shown great potential in accelerating diffusion tensor imaging (DTI).
no code implementations • 3 Jan 2024 • Jing Yang, Jian Cheng, Cheng Li, Wenxin Fan, Juan Zou, Ruoyou Wu, Shanshan Wang
Diffusion Magnetic Resonance Imaging (dMRI) plays a crucial role in the noninvasive investigation of tissue microstructural properties and structural connectivity in the \textit{in vivo} human brain.
no code implementations • 2 Dec 2023 • Zhanglu Hou, Juan Zou, Gan Ruan, YuAn Liu, Yizhang Xia
Evolutionary algorithms face significant challenges when dealing with dynamic multi-objective optimization because Pareto optimal solutions and/or Pareto optimal fronts change.
no code implementations • 14 Oct 2023 • Juan Zou, Shenghong Wu, Yizhang Xia, Weiwei Jiang, Zeping Wu, Jinhua Zheng
In our algorithm, a new cell-based search space and an effective two-stage encoding method are designed to represent cells and neural network structures.
no code implementations • 27 Aug 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang
Heterogeneous data captured by different scanning devices and imaging protocols can affect the generalization performance of the deep learning magnetic resonance (MR) reconstruction model.
no code implementations • 21 Jul 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Shanshan Wang
Centralized training methods have shown promising results in MR image reconstruction, but privacy concerns arise when gathering data from multiple institutions.
no code implementations • 10 May 2023 • Juan Zou, Cheng Li, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang
SSFedMRI explores the physics-based contrastive reconstruction networks in each client to realize cross-site collaborative training in the absence of fully sampled data.
no code implementations • 15 Apr 2023 • Ruoyou Wu, Cheng Li, Juan Zou, Qiegen Liu, Hairong Zheng, Shanshan Wang
However, high heterogeneity exists in the data from different centers, and existing federated learning methods tend to use average aggregation methods to combine the client's information, which limits the performance and generalization capability of the trained models.
no code implementations • 24 Nov 2022 • Xue Liu, Juan Zou, Xiawu Zheng, Cheng Li, Hairong Zheng, Shanshan Wang
Then, we design an effective self-supervised training data refinement method to reduce this data bias.
no code implementations • 8 Aug 2022 • Juan Zou, Cheng Li, Sen Jia, Ruoyou Wu, Tingrui Pei, Hairong Zheng, Shanshan Wang
Lately, deep learning has been extensively investigated for accelerating dynamic magnetic resonance (MR) imaging, with encouraging progresses achieved.
1 code implementation • 3 Feb 2022 • Shanshan Wang, Ruoyou Wu, Cheng Li, Juan Zou, Ziyao Zhang, Qiegen Liu, Yan Xi, Hairong Zheng
However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited.
no code implementations • 14 Dec 2020 • Yanjun Chen, Juan Zou, Zipeng Cheng, Binguang He
We explore the effects of the density dependence of symmetry energy on the dynamical instabilities and crust-core phase transition in the cold and warm neutron stars in the RMF theory with point-coupling interactions using the Vlasov approach.
Nuclear Theory