1 code implementation • 21 Nov 2023 • Ruimin Feng, Qing Wu, Jie Feng, Huajun She, Chunlei Liu, Yuyao Zhang, Hongjiang Wei
Benefiting from the powerful continuous representation and joint estimation of the MRI image and coil sensitivities, IMJENSE outperforms conventional image or k-space domain reconstruction algorithms.
no code implementations • 16 Jul 2023 • Hongyu Ding, Yuanze Tang, Qing Wu, Bo wang, Chunlin Chen, Zhi Wang
Existing reward shaping methods for goal-conditioned RL are typically built on distance metrics with a linear and isotropic distribution, which may fail to provide sufficient information about the ever-changing environment with high complexity.
1 code implementation • NeurIPS 2023 • Qing Wu, Lixuan Chen, Ce Wang, Hongjiang Wei, S. Kevin Zhou, Jingyi Yu, Yuyao Zhang
In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
no code implementations • 2 May 2023 • Haonan Zhang, Yuhan Zhang, Qing Wu, Jiangjie Wu, Zhiming Zhen, Feng Shi, Jianmin Yuan, Hongjiang Wei, Chen Liu, Yuyao Zhang
The anisotropic volume's high-resolution (HR) plane is used to build the HR-LR image pairs for model training.
no code implementations • 31 Dec 2022 • Jie Feng, Ruimin Feng, Qing Wu, Zhiyong Zhang, Yuyao Zhang, Hongjiang Wei
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
no code implementations • 23 Oct 2022 • Qing Wu, Xin Li, Hongjiang Wei, Jingyi Yu, Yuyao Zhang
NeRF-based SVCT methods represent the desired CT image as a continuous function of spatial coordinates and train a Multi-Layer Perceptron (MLP) to learn the function by minimizing loss on the SV sinogram.
no code implementations • 19 Oct 2022 • Ruimin Feng, Qing Wu, Yuyao Zhang, Hongjiang Wei
This function was parameterized by a neural network and learned directly from the measured k-space itself without additional fully sampled high-quality training data.
no code implementations • 14 Sep 2022 • Lixuan Chen, Jiangjie Wu, Qing Wu, Hongjiang Wei, Yuyao Zhang
Using implicit neural representation, we construct a continuous and noise-free longitudinal fetus brain atlas as a function of the 4D spatial-temporal coordinate.
no code implementations • 14 Sep 2022 • Xuanyu Tian, Qing Wu, Hongjiang Wei, Yuyao Zhang
Experimental results of simulated noise and real microscopy noise removal show that Noise2SR outperforms two blind-spot based self-supervised deep learning image denoising methods.
1 code implementation • 12 Sep 2022 • Qing Wu, Ruimin Feng, Hongjiang Wei, Jingyi Yu, Yuyao Zhang
Compared with recent related works that solve similar problems using implicit neural representation network (INR), our essential contribution is an effective and simple re-projection strategy that pushes the tomography image reconstruction quality over supervised deep learning CT reconstruction works.
1 code implementation • 27 Oct 2021 • Qing Wu, Yuwei Li, Yawen Sun, Yan Zhou, Hongjiang Wei, Jingyi Yu, Yuyao Zhang
In the ArSSR model, the reconstruction of HR images with different up-scaling rates is defined as learning a continuous implicit voxel function from the observed LR images.
no code implementations • 29 Jun 2021 • Qing Wu, Yuwei Li, Lan Xu, Ruiming Feng, Hongjiang Wei, Qing Yang, Boliang Yu, Xiaozhao Liu, Jingyi Yu, Yuyao Zhang
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction.
no code implementations • 24 Dec 2020 • Sundar Shrestha, Anand Koirala, Maksym Spiryagin, Qing Wu
The surface roughness between the wheel and rail has a huge influence on rolling noise level.
no code implementations • 26 Oct 2019 • Zhilin Yu, Chao Wang, Xin Wang, Qing Wu, Yong Zhao, Xundong Wu
Modern deep neural networks rely on overparameterization to achieve state-of-the-art generalization.
no code implementations • 14 Aug 2019 • Zhiming Li, Qing Wu, Kun Qian
Specifically, in terms of BLEU-4 and Word Error Rate (WER), our performance has reached 94. 50% and 2. 65% on the redundant test set; 92. 30% and 3. 48% on the purified test set.
no code implementations • NeurIPS 2018 • Xundong Wu, Xiangwen Liu, Wei Li, Qing Wu
In this study, we model such local nonlinearity of dendritic trees with our dendritic neural network (DENN) structure and apply this structure to typical machine learning tasks.
no code implementations • 22 Jun 2018 • Zhongping Ji, Xiao Qi, Yigang Wang, Gang Xu, Peng Du, Qing Wu
In this paper, we propose a deep learning based reconstruction of 3D human body shape from 2D orthographic views.
1 code implementation • 30 May 2018 • Can Li, Zhongrui Wang, Mingyi Rao, Daniel Belkin, Wenhao Song, Hao Jiang, Peng Yan, Yunning Li, Peng Lin, Miao Hu, Ning Ge, John Paul Strachan, Mark Barnell, Qing Wu, R. Stanley Williams, J. Joshua Yang, Qiangfei Xia
Recent breakthroughs in recurrent deep neural networks with long short-term memory (LSTM) units has led to major advances in artificial intelligence.
Emerging Technologies Applied Physics
no code implementations • 27 May 2017 • Chang Song, Hsin-Pai Cheng, Huanrui Yang, Sicheng Li, Chunpeng Wu, Qing Wu, Hai Li, Yiran Chen
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
no code implementations • 3 Mar 2017 • Chaofei Yang, Qing Wu, Hai Li, Yiran Chen
A countermeasure is also designed to detect such poisoning attack methods by checking the loss of the target model.
no code implementations • 3 Apr 2016 • Wei Wen, Chunpeng Wu, Yandan Wang, Kent Nixon, Qing Wu, Mark Barnell, Hai Li, Yiran Chen
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency.