Author
Xiaobo Qu
Other affiliations: Shantou University, National University of Singapore, Chinese Ministry of Education ...read more
Bio: Xiaobo Qu is an academic researcher from Chalmers University of Technology. The author has contributed to research in topics: Compressed sensing & Iterative reconstruction. 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 published on a yearly basis
Papers
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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.
329 citations
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.
Abstract: Mild cognitive impairment (MCI) is the prodromal stage of Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of converting to AD is crucial for effective treatments. In this study, a deep learning approach based on convolutional neural networks (CNN), is designed to accurately predict MCI-to-AD conversion with magnetic resonance imaging (MRI) data. First, MRI images are prepared with age-correction and other processing. Second, local patches, which are assembled into 2.5 dimensions, are extracted from these images. Then, the patches from AD and normal controls (NC) are used to train a CNN to identify deep learning features of MCI subjects. After that, structural brain image features are mined with FreeSurfer to assist CNN. Finally, both types of features are fed into an extreme learning machine classifier to predict the AD conversion. The proposed approach is validated on the standardized MRI datasets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. This approach achieves an accuracy of 79.9% and an area under the receiver operating characteristic curve (AUC) of 86.1% in leave-one-out cross validations. Compared with other state-of-the-art methods, the proposed one outperforms others with higher accuracy and AUC, while keeping a good balance between the sensitivity and specificity. Results demonstrate great potentials of the proposed CNN-based approach for the prediction of MCI-to-AD conversion with solely MRI data. Age correction and assisted structural brain image features can boost the prediction performance of CNN.
253 citations
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.
Abstract: When a bus is late and behind schedule, the stop-skipping scheme allows the bus vehicle to skip one or more stops to reduce its travel time. The deadheading problem is a special case of the stop-skipping problem, allowing a bus vehicle to skip stops between the dispatching terminal point and a designated stop. At the planning level, the optimal operating plans for these two schemes should be tackled for the benefits of bus operator as well as passengers. This paper aims to propose a methodology for this objective. Thus, three objectives are first proposed to reflect the benefits of bus operator and/or passengers, including minimizing the total waiting time, total in-vehicle travel time and total operating cost. Then, assuming random bus travel time, the stop-skipping is formulated as an optimization model minimizing the weighted sum of the three objectives. The deadheading problem can be formulated via the same minimization model further adding several new constraints. Then, a Genetic Algorithm Incorporating Monte Carlo Simulation is proposed to solve the optimization model. As validated by a numerical example, the proposed algorithm can obtain a satisfactory solution close to the global optimum.
251 citations
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.
242 citations
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.
240 citations
Cited by
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TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
10,141 citations
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30 Dec 2011
TL;DR: This table lists the most common surnames in the United States used to be Anglicised as "United States", then changed to "United Kingdom" in the 1990s.
Abstract: OUTPU T 29 OUTPU T 30 OUTPU T 31 OUTPU T 32 OUTPU T 25 OUTPU T 26 OUTPU T 27 OUTPU T 28 OUTPU T 21 OUTPU T 22 OUTPU T 23 OUTPU T 24 OUTPU T 17 OUTPU T 18 OUTPU T 19 OUTPU T 20 OUTPU T 13 OUTPU T 14 OUTPU T 15 OUTPU T 16 OUTPU T 9 OUTPU T 10 OUTPU T 11 OUTPU T 12 OUTPU T 5 OUTPU T 6 OUTPU T 7 OUTPU T 8 OUTPU T 1 OUTPU T 2 OUTPU T 3 OUTPU T 4 29 30 31 32 25 26 27 28 21 22 23 24 17 18 19 20 13 14 15 16 9
1,662 citations