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Xiaobo Qu
Researcher at Chalmers University of Technology
Publications - 332
Citations - 9336
Xiaobo Qu is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Computer science & Compressed sensing. The author has an hindex of 42, co-authored 273 publications receiving 6262 citations. Previous affiliations of Xiaobo Qu include Shantou University & National University of Singapore.
Papers
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Journal ArticleDOI
Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator
TL;DR: This paper designs a patch-based nonlocal operator (PANO) to sparsify magnetic resonance images by making use of the similarity of image patches to achieve lower reconstruction error and higher visual quality than conventional CS-MRI methods.
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Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer’s Disease Prediction From Mild Cognitive Impairment
Weiming Lin,Tong Tong,Qinquan Gao,Di Guo,Xiaofeng Du,Yonggui Yang,Gang Guo,Min Xiao,Min Du,Xiaobo Qu +9 more
TL;DR: A deep learning approach based on convolutional neural networks, designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data, outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity.
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Bus stop-skipping scheme with random travel time
TL;DR: In this paper, a genetic algorithm incorporating Monte Carlo simulation is proposed to solve the problem of deadheading in a special case of the stop-skipping problem, allowing a bus vehicle to skip stops between the dispatching terminal point and a designated stop.
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Ship collision risk assessment for the Singapore Strait
Xiaobo Qu,Qiang Meng,Li Suyi +2 more
TL;DR: It can be concluded that Legs 4W, 5W, 11E, and 12E are the most risky legs in the Strait and the ship collision risk reduction solutions should be prioritized being implemented in these four legs.
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Undersampled MRI reconstruction with patch-based directional wavelets
TL;DR: Simulation results on phantom and in vivo data indicate that the proposed patch-based directional wavelets method outperforms conventional compressed sensing MRI methods in preserving the edges and suppressing the noise.