1 code implementation • 7 Oct 2020 • Feng Wang, Huaping Liu, Di Guo, Fuchun Sun
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
1 code implementation • 29 Dec 2020 • Zi Wang, Di Guo, Zhangren Tu, Yihui Huang, Yirong Zhou, Jian Wang, Liubin Feng, Donghai Lin, Yongfu You, Tatiana Agback, Vladislav Orekhov, Xiaobo Qu
The non-uniform sampling is a powerful approach to enable fast acquisition but requires sophisticated reconstruction algorithms.
1 code implementation • 10 Mar 2020 • Yuhong Deng, Di Guo, Xiaofeng Guo, Naifu Zhang, Huaping Liu, Fuchun Sun
In this paper, we propose a novel task, Manipulation Question Answering (MQA), where the robot performs manipulation actions to change the environment in order to answer a given question.
1 code implementation • 25 Jul 2023 • Zi Wang, Xiaotong Yu, Chengyan Wang, Weibo Chen, Jiazheng Wang, Ying-Hua Chu, Hongwei Sun, Rushuai Li, Peiyong Li, Fan Yang, Haiwei Han, Taishan Kang, Jianzhong Lin, Chen Yang, Shufu Chang, Zhang Shi, Sha Hua, Yan Li, Juan Hu, Liuhong Zhu, Jianjun Zhou, Meijing Lin, Jiefeng Guo, Congbo Cai, Zhong Chen, Di Guo, Guang Yang, Xiaobo Qu
We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%.
no code implementations • 6 Apr 2016 • Jiaxi Ying, Hengfa Lu, Qingtao Wei, Jian-Feng Cai, Di Guo, Jihui Wu, Zhong Chen, Xiaobo Qu
Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.
no code implementations • 10 Mar 2015 • Zhifang Zhan, Jian-Feng Cai, Di Guo, Yunsong Liu, Zhong Chen, Xiaobo Qu
The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods.
no code implementations • 29 Apr 2015 • Yunsong Liu, Zhifang Zhan, Jian-Feng Cai, Di Guo, Zhong Chen, Xiaobo Qu
It has been shown that, redundant image representations, e. g. tight frames, can significantly improve the image quality.
no code implementations • 9 Apr 2019 • Xiaobo Qu, Yihui Huang, Hengfa Lu, Tianyu Qiu, Di Guo, Tatiana Agback, Vladislav Orekhov, Zhong Chen
Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time.
no code implementations • 17 Sep 2019 • Xinlin Zhang, Hengfa Lu, Di Guo, Lijun Bao, Feng Huang, Qin Xu, Xiaobo Qu
The pFISTA, a simple and efficient algorithm for sparse reconstruction, has been successfully extended to parallel imaging.
no code implementations • 24 Sep 2019 • Tieyuan Lu, Xinlin Zhang, Yihui Huang, Yonggui Yang, Gang Guo, Lijun Bao, Feng Huang, Di Guo, Xiaobo Qu
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time.
no code implementations • 13 Jan 2020 • Dicheng Chen, Zi Wang, Di Guo, Vladislav Orekhov, Xiaobo Qu
In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
no code implementations • 30 Apr 2020 • Sinan Tan, Huaping Liu, Di Guo, Xin-Yu Zhang, Fuchun Sun
Embodiment is an important characteristic for all intelligent agents (creatures and robots), while existing scene description tasks mainly focus on analyzing images passively and the semantic understanding of the scenario is separated from the interaction between the agent and the environment.
no code implementations • ECCV 2020 • Sinan Tan, Weilai Xiang, Huaping Liu, Di Guo, Fuchun Sun
We investigate a new AI task --- Multi-Agent Interactive Question Answering --- where several agents explore the scene jointly in interactive environments to answer a question.
no code implementations • 18 Sep 2020 • Le Xiao, Xiaoting Li, Datao Gong, Jinghong Chen, Di Guo, Huiqin He, Suen Hou, Guangming Huang, Chonghan Liu, Tiankuan Liu, Xiangming Sun, Ping-Kun Teng, Bozorgmehr Vosooghi, Annie C. Xiang, Jingbo Ye, Yang You, Zhiheng Zuo
In this paper, we present the design and test results of LOCx2, a transmitter ASIC for the ATLAS Liquid Argon Calorimeter trigger upgrade.
Instrumentation and Detectors
no code implementations • NeurIPS 2020 • Feng Wang, Huaping Liu, Di Guo, Sun Fuchun
In this paper, we propose Invariance Propagation to focus on learning representations invariant to category-level variations, which are provided by different instances from the same category.
no code implementations • 17 Nov 2020 • Fan Yang, Chao Yang, Di Guo, Huaping Liu, Fuchun Sun
Robots have limited adaptation ability compared to humans and animals in the case of damage.
no code implementations • 13 Jul 2020 • Yihui Huang, Jinkui Zhao, Zi Wang, Vladislav Orekhov, Di Guo, Xiaobo Qu
Exponential is a basic signal form, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing.
no code implementations • 26 Jan 2021 • Dicheng Chen, Wanqi Hu, Huiting Liu, Yirong Zhou, Tianyu Qiu, Yihui Huang, Zi Wang, Jiazheng Wang, Liangjie Lin, Zhigang Wu, Hao Chen, Xi Chen, Gen Yan, Di Guo, Jianzhong Lin, Xiaobo Qu
A deep learning model, Refusion Long Short-Term Memory (ReLSTM), was designed to learn the mapping from the low SNR time-domain data (24 SA) to the high SNR one (128 SA).
no code implementations • 18 Apr 2021 • Yirong Zhou, Chen Qian, Yi Guo, Zi Wang, Jian Wang, Biao Qu, Di Guo, Yongfu You, Xiaobo Qu
Machine learning and artificial intelligence have shown remarkable performance in accelerated magnetic resonance imaging (MRI).
no code implementations • 24 Jul 2021 • Xinlin Zhang, Hengfa Lu, Di Guo, Zongying Lai, Huihui Ye, Xi Peng, Bo Zhao, Xiaobo Qu
The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time.
no code implementations • 20 Sep 2021 • Xinzhu Liu, Di Guo, Huaping Liu, Fuchun Sun
In this paper, we propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects.
no code implementations • 9 Dec 2021 • Zi Wang, Chen Qian, Di Guo, Hongwei Sun, Rushuai Li, Bo Zhao, Xiaobo Qu
Deep learning has shown astonishing performance in accelerated magnetic resonance imaging (MRI).
1 code implementation • 23 Jan 2022 • Xiaoli Liu, Jianqin Yin, Di Guo, Huaping Liu
Next, we build a bi-directional semantic graph for the teacher network and a single-directional semantic graph for the student network to model rich ASCK among partial videos.
no code implementations • 26 Jan 2022 • Sinan Tan, Mengmeng Ge, Di Guo, Huaping Liu, Fuchun Sun
In the Vision-and-Language Navigation task, the embodied agent follows linguistic instructions and navigates to a specific goal.
no code implementations • 28 Mar 2022 • Chen Qian, Zi Wang, Xinlin Zhang, Boxuan Shi, Boyu Jiang, Ran Tao, Jing Li, Yuwei Ge, Taishan Kang, Jianzhong Lin, Di Guo, Xiaobo Qu
Conclusion: The explicit phase model PAIR with complementary priors has a good performance on challenging reconstructions under inter-shot motions between shots and a low signal-to-noise ratio.
no code implementations • 20 Oct 2022 • Chen Qian, Yuncheng Gao, Mingyang Han, Zi Wang, Dan Ruan, Yu Shen, Yaping Wu, Yirong Zhou, Chengyan Wang, Boyu Jiang, Ran Tao, Zhigang Wu, Jiazheng Wang, Liuhong Zhu, Yi Guo, Taishan Kang, Jianzhong Lin, Tao Gong, Chen Yang, Guoqiang Fei, Meijin Lin, Di Guo, Jianjun Zhou, Meiyun Wang, Xiaobo Qu
In conclusion, PIDD presents a novel deep learning framework by exploiting the power of MRI physics, providing a cost-effective and explainable way to break the data bottleneck in deep learning medical imaging.
no code implementations • 23 Oct 2022 • Zi Wang, Haoming Fang, Chen Qian, Boxuan Shi, Lijun Bao, Liuhong Zhu, Jianjun Zhou, Wenping Wei, Jianzhong Lin, Di Guo, Xiaobo Qu
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
no code implementations • 24 Nov 2022 • Yihui Huang, Zi Wang, Xinlin Zhang, Jian Cao, Zhangren Tu, Meijin Lin, Di Guo, Xiaobo Qu
Undersampling can accelerate the signal acquisition but at the cost of bringing in artifacts.
no code implementations • 4 Dec 2022 • Yirong Zhou, Chen Qian, Jiayu Li, Zi Wang, Yu Hu, Biao Qu, Liuhong Zhu, Jianjun Zhou, Taishan Kang, Jianzhong Lin, Qing Hong, Jiyang Dong, Di Guo, Xiaobo Qu
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
no code implementations • 29 Dec 2022 • Jian Cao, Chen Qian, Yihui Huang, Dicheng Chen, Yuncheng Gao, Jiyang Dong, Di Guo, Xiaobo Qu
Recent theory starts to explain implicit regularization with the model of deep matrix factorization (DMF) and analyze the trajectory of discrete gradient dynamics in the optimization process.
no code implementations • 21 Feb 2023 • Yuhong Deng, Xiaofeng Guo, Yixuan Wei, Kai Lu, Bin Fang, Di Guo, Huaping Liu, Fuchun Sun
A composite robotic hand composed of a suction cup and a gripper is designed for grasping the object stably.
no code implementations • 14 Apr 2023 • Chunyan Xiong, Mengli Lu, Xiaotong Yu, Jian Cao, Zhong Chen, Di Guo, Xiaobo Qu
Soft-thresholding has been widely used in neural networks.
no code implementations • 16 Jun 2023 • Dicheng Chen, Meijin Lin, Huiting Liu, Jiayu Li, Yirong Zhou, Taishan Kang, Liangjie Lin, Zhigang Wu, Jiazheng Wang, Jing Li, Jianzhong Lin, Xi Chen, Di Guo, Xiaobo Qu
Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification.
no code implementations • 19 Jun 2023 • Xiaodie Chen, Jiayu Li, Dicheng Chen, Yirong Zhou, Zhangren Tu, Meijin Lin, Taishan Kang, Jianzhong Lin, Tao Gong, Liuhong Zhu, Jianjun Zhou, Lin Ou-yang, Jiefeng Guo, Jiyang Dong, Di Guo, Xiaobo Qu
We have shared our cloud platform at MRSHub, providing free access and service for two years.
no code implementations • 26 Jul 2023 • Xinzhu Liu, Di Guo, Huaping Liu
To study collaboration among heterogeneous agents, we propose the heterogeneous multi-agent tidying-up task, in which multiple heterogeneous agents with different capabilities collaborate with each other to detect misplaced objects and place them in reasonable locations.
no code implementations • 12 Sep 2023 • Di Guo, Sijin Li, Jun Liu, Zhangren Tu, Tianyu Qiu, Jingjing Xu, Liubin Feng, Donghai Lin, Qing Hong, Meijin Lin, Yanqin Lin, Xiaobo Qu
Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation.
no code implementations • 21 Sep 2023 • Qingrui Cai, Liuhong Zhu, Jianjun Zhou, Chen Qian, Di Guo, Xiaobo Qu
PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data.
no code implementations • 18 Oct 2023 • Yirong Zhou, Yanhuang Wu, Yuhan Su, Jing Li, Jianyun Cai, Yongfu You, Di Guo, Xiaobo Qu
The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw Data (ISMRMRD) format.
no code implementations • 21 Oct 2023 • Di Guo, Runmin Xu, Jinyu Wu, Meijin Lin, Xiaofeng Du, Xiaobo Qu
Nuclear magnetic resonance (NMR) spectroscopy serves as an important tool to analyze chemicals and proteins in bioengineering.
no code implementations • 24 Feb 2024 • Zi Wang, Min Xiao, Yirong Zhou, Chengyan Wang, Naiming Wu, Yi Li, Yiwen Gong, Shufu Chang, Yinyin Chen, Liuhong Zhu, Jianjun Zhou, Congbo Cai, He Wang, Di Guo, Guang Yang, Xiaobo Qu
This challenge leads to necessitate extensive training data in many deep learning reconstruction methods.
no code implementations • 15 Mar 2024 • Kangyao Huang, Di Guo, Xinyu Zhang, Xiangyang Ji, Huaping Liu
It is common for us to feel pressure in a competition environment, which arises from the desire to obtain success comparing with other individuals or opponents.