41 results on '"Shihui Ying"'
Search Results
2. Self-supervised learning with application for infant cerebellum segmentation and analysis
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Yue Sun, Limei Wang, Kun Gao, Shihui Ying, Weili Lin, Kathryn L. Humphreys, Gang Li, Sijie Niu, Mingxia Liu, and Li Wang
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Science - Abstract
Abstract Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.
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- 2023
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3. RBF-Assisted Hybrid Neural Network for Solving Partial Differential Equations
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Ying Li, Wei Gao, and Shihui Ying
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partial differential equations ,radial basis function ,physics-informed neural networks ,numerical solution ,Mathematics ,QA1-939 - Abstract
In scientific computing, neural networks have been widely used to solve partial differential equations (PDEs). In this paper, we propose a novel RBF-assisted hybrid neural network for approximating solutions to PDEs. Inspired by the tendency of physics-informed neural networks (PINNs) to become local approximations after training, the proposed method utilizes a radial basis function (RBF) to provide the normalization and localization properties to the input data. The objective of this strategy is to assist the network in solving PDEs more effectively. During the RBF-assisted processing part, the method selects the center points and collocation points separately to effectively manage data size and computational complexity. Subsequently, the RBF processed data are put into the network for predicting the solutions to PDEs. Finally, a series of experiments are conducted to evaluate the novel method. The numerical results confirm that the proposed method can accelerate the convergence speed of the loss function and improve predictive accuracy.
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- 2024
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4. A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding
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Yanbin He, Zhiyang Lu, Jun Wang, Shihui Ying, and Jun Shi
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Motor imagery ,electroencephalography ,self-supervised learning ,multi-layer perceptron ,channel attention ,Medical technology ,R855-855.5 ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Convolutional Neural Network (CNN) is commonly used for the Electroencephalogram (EEG) based motor-imagery (MI) decoding. However, its performance is generally limited due to the small size sample problem. An alternative way to address such issue is to segment EEG trials into small slices for data augmentation, but this approach usually inevitably loses the valuable long-range dependencies of temporal information in EEG signals. To this end, we propose a novel self-supervised learning (SSL) based channel attention MLP-Mixer network (S-CAMLP-Net) for MI decoding with EEG. Specifically, a new EEG slice prediction task is designed as the pretext task to capture the long-range information of EEG trials in the time domain. In the downstream task, a newly proposed MLP-Mixer is applied to the classification task for signals rather than for images. Moreover, in order to effectively learn the discriminative spatial representations in EEG slices, an attention mechanism is integrated into MLP-Mixer to adaptively estimate the importance of each EEG channel without any prior information. Thus, the proposed S-CAMLP-Net can effectively learn more long-range temporal information and global spatial features of EEG signals. Extensive experiments are conducted on the public MI-2 dataset and the BCI Competition IV Dataset 2A. The experimental results indicate that our proposed S-CAMLP-Net achieves superior classification performance over all the compared algorithms.
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- 2022
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5. Low-Rank Matrix Completion via QR-Based Retraction on Manifolds
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Ke Wang, Zhuo Chen, Shihui Ying, and Xinjian Xu
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matrix completion ,QR factorization ,gradient algorithm ,manifold ,Mathematics ,QA1-939 - Abstract
Low-rank matrix completion aims to recover an unknown matrix from a subset of observed entries. In this paper, we solve the problem via optimization of the matrix manifold. Specially, we apply QR factorization to retraction during optimization. We devise two fast algorithms based on steepest gradient descent and conjugate gradient descent, and demonstrate their superiority over the promising baseline with the ratio of at least 24%.
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- 2023
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6. A Local-to-Global Metric Learning Framework From the Geometric Insight
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Yaxin Peng, Nijing Zhang, Ying Li, and Shihui Ying
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Local metric learning ,semi-supervised method ,partition of unity ,intrinsic steep descent method ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Metric plays a key role in the description of similarity between samples. An appropriate metric for data can well represent their distribution and further promote the performance of learning tasks. In this paper, to better describe the heterogeneous distributions of data, we propose a semi-supervised local-to-global metric learning framework from the geometric insight. Our contributions can be summarized as: Firstly, to enlarge the application scope of local metric learning, we introduce the unsupervised information as the regularization term into our smoothly glued nonlinear metric model. Secondly, we propose two different nonlinear semi-supervised metric learning models with two different loss terms, and find that the smooth loss performs better than the hinge loss by comparison results. Thirdly, we have established not only two metric learning models, but also a nonlinear metric learning framework based on local metrics, which includes supervised and semi-supervised as well as linear and nonlinear metric learning. Moreover, we present an intrinsic steepest descent algorithm on the positive definite manifold for implementation of our semi-supervised nonlinear metric learning models with smooth triplet constrain loss. Finally, we compare our approaches with several state-of-the-art methods on a variety of datasets. The results validate that the robustness and accuracy of classification are both improved under our metrics.
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- 2020
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7. An Accelerated and Robust Partial Registration Algorithm for Point Clouds
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Xin Wang, Xiaohuang Zhu, Shihui Ying, and Chaomin Shen
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Point clouds ,GPU parallel ,partial registration ,coarse-to-fine ,trimmed strategy ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Partial registration for point clouds plays an important role in various fields such as 3D mapping reconstruction, remote sensing, unmanned driving, and cultural heritage protection. Unfortunately, partial registration is challenging due to difficulties such as the low overlap ratio of two point clouds and the perturbation in the orderless and sparse 3D point clouds. Thus, a variety of the 3D shape context descriptors are introduced for finding the optimal matching. However, extracting geometric features and descriptors are time consuming and easily degenerated by noise. To overcome these problems, we introduce a parallel coarse-to-fine partial registration method. Our contributions can be summarized as: Firstly, a robust coarse trimmed method is proposed to estimate the coarse overlap area and the initial transformation via fast bilateral denoising and parallel point feature histogram (PPFH) descriptor aligning. Secondly, an accelerated fine registration procedure is conducted by a parallel trimmed iterative closest point (PTrICP) method. Moreover, most parts of our coarse-to-fine workflow are accelerated under the Graphics Processing Unit (GPU) parallel execution mode for efficiency. Thirdly, we extend our method from the rigid registration to the isotropic scaling registration, which improves its applicability. Experiments have demonstrated that our method is feasible and robust in various situations, including the low overlap ratio, outlier, noise and scaling.
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- 2020
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8. A Coarse-to-Fine Generalized-ICP Algorithm With Trimmed Strategy
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Xin Wang, Yun Li, Yaxin Peng, and Shihui Ying
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Registration ,modified GICP ,trimmed method ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
In this paper, we introduce a modified Generalized Iterative Closest Point (GICP) algorithm by presenting a coarse-to-fine strategy. Our contributions can be summarized as: Firstly, we use adaptively a plane-to-plane probabilistic matching model by gradually reducing the neighborhood range for given two point sets. It is an inner coarse-to-fine iteration process. Secondly, we use an outer coarse-to-fine strategy to bridge the point-to-point and plane-to-plane registration for refining the matching. Thirdly, we use the trimmed method to gradually eliminate the effects of incorrect correspondences, which improves the robustness of the methods especially for the low overlap cases. Moreover, we also extend our method to the scale registration case. Finally, we conduct extensive experiments to demonstrate that our method is more reliable and robust in various situations, including missing points, noise and different scale factors. Experimental results show that our approach outperforms several state-of-the-art registration methods.
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- 2020
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9. Intrinsic Metric Learning With Subspace Representation
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Lipeng Cai, Shihui Ying, Yaxin Peng, Changzhou He, and Shaoyi Du
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Metric learning ,subspace representation ,low-rank optimization ,structure preserving ,image classification ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
The accuracy of classification and retrieval significantly depends on the metric used to compute the similarity between samples. For preserving the geometric structure, the symmetric positive definite (SPD) manifold is introduced into the metric learning problem. However, the SPD constraint is too strict to describe the real data distribution. In this paper, we extend the intrinsic metric learning problem to semi-definite case, by which the data distribution is better described for various classification tasks. First, we formulate the metric learning as a minimization problem to the SPD manifold on subspace, which not only considers to balance the information between inner classes and inter classes by an adaptive tradeoff parameter but also improves the robustness by the low-rank subspaces presentation. Thus, it benefits to design a structure-preserving algorithm on subspace by using the geodesic structure of the SPD subspace. To solve this model, we develop an iterative strategy to update the intrinsic metric and the subspace structure, respectively. Finally, we compare our proposed method with ten state-of-the-art methods on four data sets. The numerical results validate that our method can significantly improve the description of the data distribution, and hence, the performance of the image classification task.
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- 2019
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10. DWNN: Deep Wavelet Neural Network for Solving Partial Differential Equations
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Ying Li, Longxiang Xu, and Shihui Ying
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partial differential equations ,wavelet transforms ,deep neural network ,numerical solution ,Mathematics ,QA1-939 - Abstract
In this paper, we propose a deep wavelet neural network (DWNN) model to approximate the natural phenomena that are described by some classical PDEs. Concretely, we introduce wavelets to deep architecture to obtain a fine feature description and extraction. That is, we constructs a wavelet expansion layer based on a family of vanishing momentum wavelets. Second, the Gaussian error function is considered as the activation function owing to its fast convergence rate and zero-centered output. Third, we design the cost function by considering the residual of governing equation, the initial/boundary conditions and an adjustable residual term of observations. The last term is added to deal with the shock wave problems and interface problems, which is conducive to rectify the model. Finally, a variety of numerical experiments are carried out to demonstrate the effectiveness of the proposed approach. The numerical results validate that our proposed method is more accurate than the state-of-the-art approach.
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- 2022
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11. Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks
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Chenwei Cai, Lvda Wang, and Shihui Ying
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image registration ,diffeomorphic ,brain MRI ,multi-label segmentation masks ,spatially adaptive parameters ,Mathematics ,QA1-939 - Abstract
Image registration aims to align two images through a spatial transformation. It plays a significant role in brain imaging analysis. In this research, we propose a symmetric diffeomorphic image registration model based on multi-label segmentation masks to solve the problems in brain MRI registration. We first introduce the similarity metric of the multi-label masks to the energy function, which improves the alignment of the brain region boundaries and the robustness to the noise. Next, we establish the model on the diffeomorphism group through the relaxation method and the inverse consistent constraint. The algorithm is designed through the local linearization and least-squares method. We then give spatially adaptive parameters to coordinate the descent of the energy function in different regions. The results show that our approach, compared with the mainstream methods, has better accuracy and noise resistance, and the transformations are more smooth and more reasonable.
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- 2022
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12. The Insular Subregions Topological Characteristics of Patients With Bipolar Depressive Disorder
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Meihui Qiu, Geya Liu, Huifeng Zhang, Yueqi Huang, Shihui Ying, Jinhong Wang, Ting Shen, and Daihui Peng
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bipolar disorder ,functional connectivity ,resting-state magnetic resonance imaging ,insular subregions ,neural substrate ,Psychiatry ,RC435-571 - Abstract
The insular cortex appears to have a crucial role in emotional processing and cognitive control in bipolar disorder (BD). However, most previous studies focused on the entire insular region of BD, neglecting the topological profile of its subregions. Our study aimed to investigate its subregion topological characteristics using the resting-state functional connectivity (rsFC) in patients with BD on depression episode. The magnetic resonance imaging (MRI) data of 28 depressed BD patients and 28 age- and gender-matched healthy controls (HCs) were acquired. We observed that compared to HCs, depressed patients with BD exhibited significantly decreased rsFC between the right ventral anterior insula (vAI) and the left middle temporal gyrus/the right angular, the right dorsal anterior insula (dAI) and the left precuneus, as well as the right posterior insula and the right lingual gyrus. Furthermore, hyperconnectivity was observed between the left dAI and the left medial frontal gyrus, as well as right dAI and left superior temporal gyrus in BD depression. However, no significant group effect was observed between aberrant FC patterns and clinical variables. These findings revealed the functional connectivity patterns of insular subregions for the depressed BD patients, suggesting the potential neural substrate of insular subregions involved in depressive episode of BD. Hence, these results may provide a neural substrate for the potential treatment target of BD on depression episode.
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- 2020
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13. Large-Scale Place Recognition Based on Camera-LiDAR Fused Descriptor
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Shaorong Xie, Chao Pan, Yaxin Peng, Ke Liu, and Shihui Ying
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place recognition ,retrieval ,sensor fusion ,deep learning ,Chemical technology ,TP1-1185 - Abstract
In the field of autonomous driving, carriers are equipped with a variety of sensors, including cameras and LiDARs. However, the camera suffers from problems of illumination and occlusion, and the LiDAR encounters motion distortion, degenerate environment and limited ranging distance. Therefore, fusing the information from these two sensors deserves to be explored. In this paper, we propose a fusion network which robustly captures both the image and point cloud descriptors to solve the place recognition problem. Our contribution can be summarized as: (1) applying the trimmed strategy in the point cloud global feature aggregation to improve the recognition performance, (2) building a compact fusion framework which captures both the robust representation of the image and 3D point cloud, and (3) learning a proper metric to describe the similarity of our fused global feature. The experiments on KITTI and KAIST datasets show that the proposed fused descriptor is more robust and discriminative than the single sensor descriptor.
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- 2020
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14. Nonlinear image registration with bidirectional metric and reciprocal regularization.
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Shihui Ying, Dan Li, Bin Xiao, Yaxin Peng, Shaoyi Du, and Meifeng Xu
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Medicine ,Science - Abstract
Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal.
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- 2017
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15. Soft shape registration under Lie group frame
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Yaxin Peng, Wei Lin, Shihui Ying, and Jigen Peng
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quadratic programming ,matrix Lie group ,optimisation problem ,expectation maximisation algorithm ,ICP framework ,iterative closest point ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Computer software ,QA76.75-76.765 - Abstract
In this study, the authors address a two‐dimensional (2D) shape registration problem on data with anisotropic‐scale deformation and noise. First, the model is formulated under the iterative closest point (ICP) framework, which is one of the most popular methods for shape registration. To overcome the effect of noise, the expectation maximisation algorithm is used to improve the model. Then, the structure of Lie groups is adopted to parameterise the proposed model, which provides a unified framework to deal with the shape registration problems. Such representation makes it possible to introduce some suitable constraints to the model, which improves the robustness of the algorithm. Thereby, the 2D shape registration problem is turned to an optimisation problem on the matrix Lie group. Furthermore, a sequence of quadratic programming is designed to approximate the solution for the model. Finally, several comparative experiments are carried out to validate that the authors’ algorithm performs well in terms of robustness, especially in the presence of outliers.
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- 2013
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16. eHUGS: Enhanced Hierarchical Unbiased Graph Shrinkage for Efficient Groupwise Registration.
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Guorong Wu, Xuewei Peng, Shihui Ying, Qian Wang, Pew-Thian Yap, Dan Shen, and Dinggang Shen
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Medicine ,Science - Abstract
Effective and efficient spatial normalization of a large population of brain images is critical for many clinical and research studies, but it is technically very challenging. A commonly used approach is to choose a certain image as the template and then align all other images in the population to this template by applying pairwise registration. To avoid the potential bias induced by the inappropriate template selection, groupwise registration methods have been proposed to simultaneously register all images to a latent common space. However, current groupwise registration methods do not make full use of image distribution information for more accurate registration. In this paper, we present a novel groupwise registration method that harnesses the image distribution information by capturing the image distribution manifold using a hierarchical graph with its nodes representing the individual images. More specifically, a low-level graph describes the image distribution in each subgroup, and a high-level graph encodes the relationship between representative images of subgroups. Given the graph representation, we can register all images to the common space by dynamically shrinking the graph on the image manifold. The topology of the entire image distribution is always maintained during graph shrinkage. Evaluations on two datasets, one for 80 elderly individuals and one for 285 infants, indicate that our method can yield promising results.
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- 2016
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17. Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities
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Xiangmin, Han, Jun, Wang, Weijun, Zhou, Cai, Chang, Shihui, Ying, and Jun, Shi
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Computer Science - Computer Vision and Pattern Recognition ,Computer Science - Machine Learning ,Statistics - Machine Learning - Abstract
Elastography ultrasound (EUS) provides additional bio-mechanical in-formation about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality im-balance problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer learning (TL) pay little attention to this special issue of clinical modality imbalance, that is, the source domain (EUS modality) has fewer labeled samples than those in the target domain (BUS modality). Moreover, these TL methods cannot fully use the label information to explore the intrinsic relation between two modalities and then guide the promoted knowledge transfer. To this end, we propose a novel doubly supervised TL network (DDSTN) that integrates the Learning Using Privileged Information (LUPI) paradigm and the Maximum Mean Discrepancy (MMD) criterion into a unified deep TL framework. The proposed algorithm can not only make full use of the shared labels to effectively guide knowledge transfer by LUPI paradigm, but also perform additional super-vised transfer between unpaired data. We further introduce the MMD criterion to enhance the knowledge transfer. The experimental results on the breast ultra-sound dataset indicate that the proposed DDSTN outperforms all the compared state-of-the-art algorithms for the BUS-based CAD., Comment: Accepted by MICCAI 2020
- Published
- 2020
18. A Weighting Method for Feature Dimension by Semisupervised Learning With Entropy
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Murong Yang, Shihui Ying, Ziyan Qin, Jigen Peng, and Dequan Jin
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Computer Networks and Communications ,business.industry ,Dimensionality reduction ,Pattern recognition ,A-weighting ,Class (biology) ,Computer Science Applications ,Weighting ,Feature Dimension ,Artificial Intelligence ,Feature (computer vision) ,Metric (mathematics) ,Artificial intelligence ,Entropy (energy dispersal) ,business ,Software ,Mathematics - Abstract
In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large. According to this, we propose a way to construct the weights of feature dimensions with the whole entropy and the innerclass entropies. The weights indicate the contribution of their corresponding feature dimensions in classification. They can be used to improve the performance of classification by giving a weighted distance metric and can be applied to dimension reduction and correlation analysis as well. Some numerical experiments are given to test the proposed method by comparing it with some other representative methods. They demonstrate that the proposed method is feasible and efficient in classification, dimension reduction, and correlation analysis.
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- 2023
19. Rotation-Invariant Point Cloud Representation for 3-D Model Recognition
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Yan Wang, Yining Zhao, Shihui Ying, Shaoyi Du, and Yue Gao
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Human-Computer Interaction ,Imaging, Three-Dimensional ,Control and Systems Engineering ,Visual Perception ,Models, Theoretical ,Electrical and Electronic Engineering ,Software ,Computer Science Applications ,Information Systems - Abstract
Three-dimensional (3-D) data have many applications in the field of computer vision and a point cloud is one of the most popular modalities. Therefore, how to establish a good representation for a point cloud is a core issue in computer vision, especially for 3-D object recognition tasks. Existing approaches mainly focus on the invariance of representation under the group of permutations. However, for point cloud data, it should also be rotation invariant. To address such invariance, in this article, we introduce a relation of equivalence under the action of rotation group, through which the representation of point cloud is located in a homogeneous space. That is, two point clouds are regarded as equivalent when they are only different from a rotation. Our network is flexibly incorporated into existing frameworks for point clouds, which guarantees the proposed approach to be rotation invariant. Besides, a sufficient analysis on how to parameterize the group SO(3) into a convolutional network, which captures a relation with all rotations in 3-D Euclidean space [Formula: see text]. We select the optimal rotation as the best representation of point cloud and propose a solution for minimizing the problem on the rotation group SO(3) by using its geometric structure. To validate the rotation invariance, we combine it with two existing deep models and evaluate them on ModelNet40 dataset and its subset ModelNet10. Experimental results indicate that the proposed strategy improves the performance of those existing deep models when the data involve arbitrary rotations.
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- 2022
20. Doubly Supervised Transfer Classifier for Computer-Aided Diagnosis With Imbalanced Modalities
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Xiangmin Han, Xiaoyan Fei, Jun Wang, Tao Zhou, Shihui Ying, Jun Shi, and Dinggang Shen
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Support Vector Machine ,Radiological and Ultrasound Technology ,Computers ,Neuroimaging ,Diagnosis, Computer-Assisted ,Electrical and Electronic Engineering ,Algorithms ,Software ,Computer Science Applications - Abstract
Transfer learning (TL) can effectively improve diagnosis accuracy of single-modal-imaging-based computer-aided diagnosis (CAD) by transferring knowledge from other related imaging modalities, which offers a way to alleviate the small-sample-size problem. However, medical imaging data generally have the following characteristics for the TL-based CAD: 1) The source domain generally has limited data, which increases the difficulty to explore transferable information for the target domain; 2) Samples in both domains often have been labeled for training the CAD model, but the existing TL methods cannot make full use of label information to improve knowledge transfer. In this work, we propose a novel doubly supervised transfer classifier (DSTC) algorithm. In particular, DSTC integrates the support vector machine plus (SVM+) classifier and the low-rank representation (LRR) into a unified framework. The former makes full use of the shared labels to guide the knowledge transfer between the paired data, while the latter adopts the block-diagonal low-rank (BLR) to perform supervised TL between the unpaired data. Furthermore, we introduce the Schatten-p norm for BLR to obtain a tighter approximation to the rank function. The proposed DSTC algorithm is evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset and the bimodal breast ultrasound image (BBUI) dataset. The experimental results verify the effectiveness of the proposed DSTC algorithm.
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- 2022
21. Kullback–Leibler Divergence Metric Learning
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Zizhao Zhang, Shihui Ying, Xibin Zhao, Shuyi Ji, Yue Gao, and Liejun Wang
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Kullback–Leibler divergence ,Similarity (geometry) ,Computer science ,02 engineering and technology ,computer.software_genre ,Matrix (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,Electrical and Electronic Engineering ,Divergence (statistics) ,0505 law ,Computer Science::Information Retrieval ,Document classification ,05 social sciences ,020207 software engineering ,Manifold ,Computer Science Applications ,Human-Computer Interaction ,Linear map ,Research Design ,Control and Systems Engineering ,Metric (mathematics) ,050501 criminology ,Method of steepest descent ,Algorithm ,computer ,Software ,Distribution (differential geometry) ,Information Systems - Abstract
The Kullback-Leibler divergence (KLD), which is widely used to measure the similarity between two distributions, plays an important role in many applications. In this article, we address the KLD metric-learning task, which aims at learning the best KLD-type metric from the distributions of datasets. Concretely, first, we extend the conventional KLD by introducing a linear mapping and obtain the best KLD to well express the similarity of data distributions by optimizing such a linear mapping. It improves the expressivity of data distribution, which means it makes the distributions in the same class close and those in different classes far away. Then, the KLD metric learning is modeled by a minimization problem on the manifold of all positive-definite matrices. To deal with this optimization task, we develop an intrinsic steepest descent method, which preserves the manifold structure of the metric in the iteration. Finally, we apply the proposed method along with ten popular metric-learning approaches on the tasks of 3-D object classification and document classification. The experimental results illustrate that our proposed method outperforms all other methods.
- Published
- 2022
22. CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence
- Author
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Nanning Zheng, Shihui Ying, Pengyu Yin, Shaoyi Du, Di Wang, and Yue Gao
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FOS: Computer and information sciences ,0209 industrial biotechnology ,Computer science ,Euclidean group ,Probabilistic logic ,Iterative closest point ,Point set registration ,02 engineering and technology ,Computer Science - Robotics ,020901 industrial engineering & automation ,Robustness (computer science) ,Outlier ,Metric (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Noise (video) ,Algorithm ,Robotics (cs.RO) - Abstract
In this paper, we propose a novel probabilistic variant of iterative closest point (ICP) dubbed as CoBigICP. The method leverages both local geometrical information and global noise characteristics. Locally, the 3D structure of both target and source clouds are incorporated into the objective function through bidirectional correspondence. Globally, error metric of correntropy is introduced as noise model to resist outliers. Importantly, the close resemblance between normal-distributions transform (NDT) and correntropy is revealed. To ease the minimization step, an on-manifold parameterization of the special Euclidean group is proposed. Extensive experiments validate that CoBigICP outperforms several well-known and state-of-the-art methods., Comment: 6 pages, 4 figures. Accepted to IROS2020
- Published
- 2023
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23. Structure Evolution on Manifold for Graph Learning
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Hai Wan, Xinwei Zhang, Yubo Zhang, Xibin Zhao, Shihui Ying, and Yue Gao
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Computational Theory and Mathematics ,Artificial Intelligence ,Applied Mathematics ,Computer Vision and Pattern Recognition ,Software - Abstract
Graph has been widely used in various applications, while how to optimize the graph is still an open question. In this paper, we propose a framework to optimize the graph structure via structure evolution on graph manifold. We first define the graph manifold and search the best graph structure on this manifold. Concretely, associated with the data features and the prediction results of a given task, we define a graph energy to measure how the graph fits the graph manifold from an initial graph structure. The graph structure then evolves by minimizing the graph energy. In this process, the graph structure can be evolved on the graph manifold corresponding to the update of the prediction results. Alternatively iterating these two processes, both the graph structure and the prediction results can be updated until converge. It achieves the suitable structure for graph learning without searching all hyperparameters. To evaluate the performance of the proposed method, we have conducted experiments on eight datasets and compared with the recent state-of-the-art methods. Experiment results demonstrate that our method outperforms the state-of-the-art methods in both transductive and inductive settings.
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- 2022
24. PGF-BIQA: Blind image quality assessment via probability multi-grained cascade forest
- Author
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Hao Liu, Ce Li, Shangang Jin, Weizhe Gao, Fenghua Liu, Shaoyi Du, and Shihui Ying
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Signal Processing ,Computer Vision and Pattern Recognition ,Software - Published
- 2023
25. Graph Learning on Millions of Data in Seconds: Label Propagation Acceleration on Graph using Data Distribution
- Author
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Yubo Zhang, Shuyi Ji, Changqing Zou, Xibin Zhao, Shihui Ying, and Yue Gao
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Computational Theory and Mathematics ,Artificial Intelligence ,Applied Mathematics ,Computer Vision and Pattern Recognition ,Software - Abstract
Graph-based semi-supervised learning methods have been used in a wide range of real-world applications. However, existing methods limited along with high computational complexity or not facilitating incremental learning, which may not be powerful to deal with large-scale data, whose scale may continuously increase, in real world. This paper proposes a new method called Data Distribution Based Graph Learning (DDGL) for semi-supervised learning on large-scale data. This method can achieve a fast and effective label propagation and supports incremental learning. The key motivation is to propagate the labels along smaller-scale data distribution model parameters, rather than directly dealing with the raw data as previous methods, which accelerate the data propagation significantly. It also improves the prediction accuracy since the loss of structure information can be alleviated in this way. To enable incremental learning, we propose an adaptive graph updating strategy when there is distribution bias between new data and already seen data. We have conducted comprehensive experiments on multiple datasets with sizes increasing from seven thousand to five million. Experimental results on the classification task on large-scale data demonstrate that our proposed DDGL method improves the classification accuracy by a large margin while consuming much less time compared to state-of-art methods.
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- 2022
26. Intrinsic Metric Learning With Subspace Representation
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Yaxin Peng, Shihui Ying, Changzhou He, Lipeng Cai, and Shaoyi Du
- Subjects
General Computer Science ,Geodesic ,Computer science ,Metric learning ,02 engineering and technology ,Positive-definite matrix ,01 natural sciences ,Intrinsic metric ,low-rank optimization ,Robustness (computer science) ,structure preserving ,0103 physical sciences ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,010306 general physics ,Contextual image classification ,General Engineering ,Linear subspace ,Manifold ,subspace representation ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,lcsh:TK1-9971 ,Algorithm ,Subspace topology ,image classification - Abstract
The accuracy of classification and retrieval significantly depends on the metric used to compute the similarity between samples. For preserving the geometric structure, the symmetric positive definite (SPD) manifold is introduced into the metric learning problem. However, the SPD constraint is too strict to describe the real data distribution. In this paper, we extend the intrinsic metric learning problem to semi-definite case, by which the data distribution is better described for various classification tasks. First, we formulate the metric learning as a minimization problem to the SPD manifold on subspace, which not only considers to balance the information between inner classes and inter classes by an adaptive tradeoff parameter but also improves the robustness by the low-rank subspaces presentation. Thus, it benefits to design a structure-preserving algorithm on subspace by using the geodesic structure of the SPD subspace. To solve this model, we develop an iterative strategy to update the intrinsic metric and the subspace structure, respectively. Finally, we compare our proposed method with ten state-of-the-art methods on four data sets. The numerical results validate that our method can significantly improve the description of the data distribution, and hence, the performance of the image classification task.
- Published
- 2019
27. The concurrent disturbance of dynamic functional and structural brain connectome in major depressive disorder: the prefronto-insular pathway
- Author
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Yan Wu, Daihui Peng, Meihui Qiu, Shihui Ying, Enchao Cong, Huifeng Zhang, Lena Palaniyappan, Yueqi Huang, Jinhong Wang, Feng Jin, Chuangxin Wu, Lei Ding, and Ye Wu
- Subjects
Bipolar Disorder ,Bipolar depression ,Posterior parietal cortex ,Major depressive disorder ,behavioral disciplines and activities ,White matter ,03 medical and health sciences ,0302 clinical medicine ,Salience network ,Connectome ,Medicine ,Humans ,Depression (differential diagnoses) ,Dynamic functional connectivity ,Depressive Disorder, Major ,business.industry ,Structural connectivity ,Brain ,Small sample ,medicine.disease ,Magnetic Resonance Imaging ,030227 psychiatry ,Psychiatry and Mental health ,Clinical Psychology ,medicine.anatomical_structure ,business ,Neuroscience ,030217 neurology & neurosurgery ,Tractography - Abstract
Background Robust evidence has shown that abnormal function networks, particularly the salience network (SN), are observed in depressed patients. Although white matter structural connectivity may predict time-varying functional connectivity, including symptom phenotype, in psychiatric disorders, there is still a gap in elucidating the concurrent dynamic functional and structural connectivity profiles of the SN in depressed patients. Methods We measured static and dynamic functional connectivity (FC) of the SN using resting-state fMRI BOLD time series in 76 subjects (21 with major depressive disorder (MDD), 27 with bipolar depression (BD), and 28 healthy controls (HC)). Hamilton Depression Scale total score was used to measure depression severity. Furthermore, we investigated the concurrent structural connectivity using diffusion kurtosis imaging (DKI)-based tractography. Results Our findings suggested that in the presence of MDD, both structural and dynamic (but not static) FC were reduced in the SN, particularly affecting the left prefronto-insular pathways (L.aPFC-L.insula). MDD patients showed decreased connectivity variability within the SN compared with HC. The aberrant dynamic FC in the prefronto-insular pathways of the SN related to severity of depressive symptoms in MDD. Furthermore, compared with BD patients, those with MDD showed significantly decreased dynamic FC in the left prefronto-parietal system (L.aPFC-lateral parietal cortex). Limitations The generalizability of our findings is, to some extent, constrained by the small sample size. Conclusions The integrity of SN connectivity, particularly the prefronto-insular pathway, appears to be a crucial signature of MDD. The perturbed dynamic interaction of SN with prefrontal regions may underlie the clinical severity in depressed patients.
- Published
- 2020
28. Large-Scale Place Recognition Based on Camera-LiDAR Fused Descriptor
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Chao Pan, Shihui Ying, Yaxin Peng, Ke Liu, and Shaorong Xie
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0209 industrial biotechnology ,Similarity (geometry) ,Computer science ,Point cloud ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,02 engineering and technology ,lcsh:Chemical technology ,Biochemistry ,Article ,Analytical Chemistry ,020901 industrial engineering & automation ,Discriminative model ,Distortion ,0202 electrical engineering, electronic engineering, information engineering ,Computer vision ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,retrieval ,sensor fusion ,business.industry ,deep learning ,Sensor fusion ,place recognition ,Atomic and Molecular Physics, and Optics ,Lidar ,Feature (computer vision) ,Metric (mathematics) ,020201 artificial intelligence & image processing ,Artificial intelligence ,business - Abstract
In the field of autonomous driving, carriers are equipped with a variety of sensors, including cameras and LiDARs. However, the camera suffers from problems of illumination and occlusion, and the LiDAR encounters motion distortion, degenerate environment and limited ranging distance. Therefore, fusing the information from these two sensors deserves to be explored. In this paper, we propose a fusion network which robustly captures both the image and point cloud descriptors to solve the place recognition problem. Our contribution can be summarized as: (1) applying the trimmed strategy in the point cloud global feature aggregation to improve the recognition performance, (2) building a compact fusion framework which captures both the robust representation of the image and 3D point cloud, and (3) learning a proper metric to describe the similarity of our fused global feature. The experiments on KITTI and KAIST datasets show that the proposed fused descriptor is more robust and discriminative than the single sensor descriptor.
- Published
- 2020
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29. Deep Doubly Supervised Transfer Network for Diagnosis of Breast Cancer with Imbalanced Ultrasound Imaging Modalities
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Weijun Zhou, Cai Chang, Jun Shi, Jun Wang, Xiangmin Han, and Shihui Ying
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Modalities ,Modality (human–computer interaction) ,medicine.diagnostic_test ,Computer science ,business.industry ,02 engineering and technology ,Machine learning ,computer.software_genre ,medicine.disease ,Lesion ,03 medical and health sciences ,0302 clinical medicine ,Breast cancer ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Ultrasound imaging ,020201 artificial intelligence & image processing ,Elastography ,Artificial intelligence ,medicine.symptom ,Transfer of learning ,business ,computer ,Breast ultrasound ,030217 neurology & neurosurgery - Abstract
Elastography ultrasound (EUS) provides additional bio-mechanical information about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality imbalance problem in computer-aided diagnosis (CAD) for breast cancers. Current transfer learning (TL) pay little attention to this special issue of clinical modality imbalance, that is, the source domain (EUS modality) has fewer labeled samples than those in the target domain (BUS modality). Moreover, these TL methods cannot fully use the label information to explore the intrinsic relation between two modalities and then guide the promoted knowledge transfer. To this end, we propose a novel doubly supervised TL network (DDSTN) that integrates the Learning Using Privileged Information (LUPI) paradigm and the Maximum Mean Discrepancy (MMD) criterion into a unified deep TL framework. The proposed algorithm can not only make full use of the shared labels to effectively guide knowledge transfer by LUPI paradigm, but also perform additional supervised transfer between unpaired data. We further introduce the MMD criterion to enhance the knowledge transfer. The experimental results on the breast ultrasound dataset indicate that the proposed DDSTN outperforms all the compared state-of-the-art algorithms for the BUS-based CAD.
- Published
- 2020
30. Lightweight adaptive weighted network for single image super-resolution
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Shihui Ying, Jun Wang, Zheng Li, Jun Shi, and Chaofeng Wang
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Computer science ,business.industry ,Computation ,Deep learning ,Residual ,Convolutional neural network ,Method of mean weighted residuals ,Signal Processing ,Redundancy (engineering) ,Weighted network ,Computer Vision and Pattern Recognition ,Artificial intelligence ,business ,Algorithm ,Software ,Block (data storage) - Abstract
Deep learning has been successfully applied to the single-image super-resolution (SISR) task with superior performance in recent years. However, most convolutional neural network (CNN) based SR models have a large number of parameters to be optimized, which requires heavy computation and thereby limits their real-world applications. In this work, a novel lightweight SR network, named Adaptive Weighted Super-Resolution Network (LW-AWSRN), is proposed to address this issue. A novel local fusion block (LFB) is developed in LW-AWSRN for efficient residual learning, which consists of several stacked adaptive weighted residual units (AWRU) and a local residual fusion unit (LRFU). Moreover, an adaptive weighted multi-scale (AWMS) module is proposed to make full use of features for the reconstruction of HR images. The AWMS module includes several convolutions with multiple scales, and the redundancy scale branch can be removed according to the contribution of adaptive weights for the lightweight network. The experimental results on the commonly used datasets show that the proposed LW-AWSRN achieves superior performance on × 2, × 3, × 4, and × 8 scale factors compared to state-of-the-art methods with similar parameters and computational overhead. It suggests that LW-AWSRN has a better trade-off between reconstruction quality and model size.
- Published
- 2021
31. Manifold Preserving: An Intrinsic Approach for Semisupervised Distance Metric Learning
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Jun Shi, Jigen Peng, Shihui Ying, Hong Qiao, Zhijie Wen, and Yaxin Peng
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Manifold alignment ,Computer Networks and Communications ,02 engineering and technology ,Semi-supervised learning ,Topology ,Data structure ,Manifold ,Computer Science Applications ,Data modeling ,Matrix (mathematics) ,Artificial Intelligence ,020204 information systems ,Metric (mathematics) ,0202 electrical engineering, electronic engineering, information engineering ,Method of steepest descent ,020201 artificial intelligence & image processing ,Software ,Mathematics - Abstract
In this paper, we address the semisupervised distance metric learning problem and its applications in classification and image retrieval. First, we formulate a semisupervised distance metric learning model by considering the metric information of inner classes and interclasses. In this model, an adaptive parameter is designed to balance the inner metrics and intermetrics by using data structure. Second, we convert the model to a minimization problem whose variable is symmetric positive-definite matrix. Third, in implementation, we deduce an intrinsic steepest descent method, which assures that the metric matrix is strictly symmetric positive-definite at each iteration, with the manifold structure of the symmetric positive-definite matrix manifold. Finally, we test the proposed algorithm on conventional data sets, and compare it with other four representative methods. The numerical results validate that the proposed method significantly improves the classification with the same computational efficiency.
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- 2017
32. Geometric Understanding for Unsupervised Subspace Learning
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Yaxin Peng, Shihui Ying, Lipeng Cai, and Changzhou He
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Computer science ,business.industry ,Pattern recognition ,Artificial intelligence ,business ,Subspace topology - Abstract
In this paper, we address the unsupervised subspace learning from a geometric viewpoint. First, we formulate the subspace learning as an inverse problem on Grassmannian manifold by considering all subspaces as points on it. Then, to make the model computable, we parameterize the Grassmannian manifold by using an orbit of rotation group action on all standard subspaces, which are spanned by the orthonormal basis. Further, to improve the robustness, we introduce a low-rank regularizer which makes the dimension of subspace as low as possible. Thus, the subspace learning problem is transferred to a minimization problem with variables of rotation and dimension. Then, we adopt the alternately iterative strategy to optimize the variables, where a structure-preserving method, based on the geodesic structure of the rotation group, is designed to update the rotation. Finally, we compare the proposed approach with six state-of-the-art methods on three different kinds of real datasets. The experimental results validate that our proposed method outperforms all compared methods.
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- 2019
33. Global Nonlinear Metric Learning by Gluing Local Linear Metrics
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Yaxin Peng, Lingfang Hu, Shihui Ying, and Chaomin Shen
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- 2018
34. Nonlinear image registration with bidirectional metric and reciprocal regularization
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Shaoyi Du, Bin Xiao, Yaxin Peng, Dan Li, Meifeng Xu, and Shihui Ying
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Geodesic ,Computer science ,Velocity ,lcsh:Medicine ,02 engineering and technology ,Regularization (mathematics) ,Diagnostic Radiology ,0302 clinical medicine ,Medicine and Health Sciences ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:Science ,media_common ,Energy functional ,Multidisciplinary ,Physics ,Radiology and Imaging ,Applied Mathematics ,Simulation and Modeling ,Classical Mechanics ,Brain ,Magnetic Resonance Imaging ,Deformation ,Physical Sciences ,020201 artificial intelligence & image processing ,Diffeomorphism ,Algorithm ,Algorithms ,Reciprocal ,Research Article ,Optimization ,Imaging Techniques ,media_common.quotation_subject ,Image registration ,Neuroimaging ,Image Analysis ,Research and Analysis Methods ,Motion ,03 medical and health sciences ,Diagnostic Medicine ,Image Interpretation, Computer-Assisted ,Humans ,Damage Mechanics ,Variables ,lcsh:R ,Biology and Life Sciences ,Nonlinear system ,Nonlinear Dynamics ,lcsh:Q ,Mathematics ,030217 neurology & neurosurgery ,Neuroscience - Abstract
Nonlinear registration is an important technique to align two different images and widely applied in medical image analysis. In this paper, we develop a novel nonlinear registration framework based on the diffeomorphic demons, where a reciprocal regularizer is introduced to assume that the deformation between two images is an exact diffeomorphism. In detail, first, we adopt a bidirectional metric to improve the symmetry of the energy functional, whose variables are two reciprocal deformations. Secondly, we slack these two deformations into two independent variables and introduce a reciprocal regularizer to assure the deformations being the exact diffeomorphism. Then, we utilize an alternating iterative strategy to decouple the model into two minimizing subproblems, where a new closed form for the approximate velocity of deformation is calculated. Finally, we compare our proposed algorithm on two data sets of real brain MR images with two relative and conventional methods. The results validate that our proposed method improves accuracy and robustness of registration, as well as the gained bidirectional deformations are actually reciprocal.
- Published
- 2017
35. Hierarchical unbiased graph shrinkage (HUGS): A novel groupwise registration for large data set
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Dinggang Shen, Shihui Ying, Guorong Wu, and Qian Wang
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Geodesic ,Cognitive Neuroscience ,Population ,Image registration ,Article ,Common space ,Pattern Recognition, Automated ,Image Processing, Computer-Assisted ,Humans ,Computer vision ,Image warping ,education ,Mathematics ,Brain Mapping ,education.field_of_study ,business.industry ,Brain ,Pattern recognition ,Magnetic Resonance Imaging ,Data set ,Neurology ,Graph (abstract data type) ,Artificial intelligence ,Diffeomorphism ,business ,Algorithms - Abstract
Normalizing all images in a large data set into a common space is a key step in many clinical and research studies, e.g., for brain development, maturation, and aging. Recently, groupwise registration has been developed for simultaneous alignment of all images without selecting a particular image as template, thus potentially avoiding bias in the registration. However, most conventional groupwise registration methods do not explore the data distribution during the image registration. Thus, their performance could be affected by large inter-subject variations in the data set under registration. To solve this potential issue, we propose to use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the groupwise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed groupwise registration method on both infant and adult data sets, by also comparing with the conventional group-mean based registration and the ABSORB methods. All experimental results show that our proposed method can achieve better performance in terms of registration accuracy and robustness.
- Published
- 2014
36. Soft shape registration under Lie group frame
- Author
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Jigen Peng, Wei Lin, Shihui Ying, and Yaxin Peng
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Mathematical optimization ,Noise ,Robustness (computer science) ,Frame (networking) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Lie group ,Iterative closest point ,Image registration ,Computer Vision and Pattern Recognition ,Quadratic programming ,Representation (mathematics) ,Software ,Mathematics - Abstract
In this study, the authors address a two-dimensional (2D) shape registration problem on data with anisotropic-scale deformation and noise. First, the model is formulated under the iterative closest point (ICP) framework, which is one of the most popular methods for shape registration. To overcome the effect of noise, the expectation maximisation algorithm is used to improve the model. Then, the structure of Lie groups is adopted to parameterise the proposed model, which provides a unified framework to deal with the shape registration problems. Such representation makes it possible to introduce some suitable constraints to the model, which improves the robustness of the algorithm. Thereby, the 2D shape registration problem is turned to an optimisation problem on the matrix Lie group. Furthermore, a sequence of quadratic programming is designed to approximate the solution for the model. Finally, several comparative experiments are carried out to validate that the authors’ algorithm performs well in terms of robustness, especially in the presence of outliers.
- Published
- 2013
37. Nonlinear Semi-Supervised Metric Learning Via Multiple Kernels and Local Topology
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Xin Li, Shaoyi Du, Shihui Ying, Yaxin Peng, and Yanqin Bai
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Mathematical optimization ,Computer Networks and Communications ,02 engineering and technology ,Semi-supervised learning ,Chebyshev distance ,Pattern Recognition, Automated ,Intrinsic metric ,Metric k-center ,03 medical and health sciences ,0302 clinical medicine ,Artificial Intelligence ,0202 electrical engineering, electronic engineering, information engineering ,Cluster Analysis ,Humans ,Computer Simulation ,Mathematics ,General Medicine ,Equivalence of metrics ,Nonlinear Dynamics ,Metric (mathematics) ,020201 artificial intelligence & image processing ,Supervised Machine Learning ,String metric ,Algorithms ,030217 neurology & neurosurgery ,Fisher information metric - Abstract
Changing the metric on the data may change the data distribution, hence a good distance metric can promote the performance of learning algorithm. In this paper, we address the semi-supervised distance metric learning (ML) problem to obtain the best nonlinear metric for the data. First, we describe the nonlinear metric by the multiple kernel representation. By this approach, we project the data into a high dimensional space, where the data can be well represented by linear ML. Then, we reformulate the linear ML by a minimization problem on the positive definite matrix group. Finally, we develop a two-step algorithm for solving this model and design an intrinsic steepest descent algorithm to learn the positive definite metric matrix. Experimental results validate that our proposed method is effective and outperforms several state-of-the-art ML methods.
- Published
- 2018
38. LIE GROUP FRAMEWORK OF ITERATIVE CLOSEST POINT ALGORITHM FOR n-D DATA REGISTRATION
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Shaoyi Du, Shihui Ying, Hong Qiao, and Jigen Peng
- Subjects
Mathematical optimization ,Optimization problem ,Linear system ,Geometric transformation ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Iterative closest point ,Lie group ,Translation (geometry) ,Dimension (vector space) ,Artificial Intelligence ,Point (geometry) ,Computer Vision and Pattern Recognition ,Algorithm ,Software ,Mathematics - Abstract
The iterative closet point (ICP) method is a dominant method for data registration that has attracted extensive attention. In this paper, a unified mathematical model of ICP based on Lie group representation is established. Under the framework, the registration problem is formulated into an optimization problem over a certain Lie group. In order to simplify the model and to reduce the dimension of parameter space, the translation part of geometric transformation is eliminated by calibrating the centers of two data sets under registration. As a result, a fast algorithm by solving an iterative linear system is designed for the optimization problem on Lie groups. Moreover, PCA and ICA methods are jointly applied to estimate the initial registration to achieve the global minimum. Finally, several illustrations and comparison experiments are presented to test the performance of the proposed algorithm.
- Published
- 2009
39. A New Approach to Weighted Graph Matching
- Author
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Jigen Peng, Kai-Jie Zheng, and Shihui Ying
- Subjects
Factor-critical graph ,Mathematical optimization ,Matching (graph theory) ,Computer science ,Floyd–Warshall algorithm ,Artificial Intelligence ,Hardware and Architecture ,Kruskal's algorithm ,3-dimensional matching ,Reverse-delete algorithm ,Bipartite graph ,Johnson's algorithm ,Computer Vision and Pattern Recognition ,Suurballe's algorithm ,Electrical and Electronic Engineering ,Algorithm ,Assignment problem ,Software ,Blossom algorithm ,Hopcroft–Karp algorithm - Abstract
SUMMARY Weighted graph matching is computationally challenging due to the combinatorial nature of the set of permutations. In this paper, a new relaxation approach to weighted graph matching is proposed, by which a new matching algorithm, named alternate iteration algorithm, is designed. It is proved that the algorithm proposed is locally convergent. Experiments are presented to show the effectiveness of the proposed algorithm. eration algorithm.
- Published
- 2009
40. Groupwise Registration via Graph Shrinkage on the Image Manifold
- Author
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Dinggang Shen, Shihui Ying, Qian Wang, and Guorong Wu
- Subjects
education.field_of_study ,Geodesic ,business.industry ,Population ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image registration ,Graph theory ,Differential geometry ,Computer Science::Computer Vision and Pattern Recognition ,Graph (abstract data type) ,Computer vision ,Diffeomorphism ,Artificial intelligence ,Image warping ,education ,business ,Mathematics - Abstract
Recently, group wise registration has been investigated for simultaneous alignment of all images without selecting any individual image as the template, thus avoiding the potential bias in image registration. However, none of current group wise registration method fully utilizes the image distribution to guide the registration. Thus, the registration performance usually suffers from large inter-subject variations across individual images. To solve this issue, we propose a novel group wise registration algorithm for large population dataset, guided by the image distribution on the manifold. Specifically, we first use a graph to model the distribution of all image data sitting on the image manifold, with each node representing an image and each edge representing the geodesic pathway between two nodes (or images). Then, the procedure of warping all images to their population center turns to the dynamic shrinking of the graph nodes along their graph edges until all graph nodes become close to each other. Thus, the topology of image distribution on the image manifold is always preserved during the group wise registration. More importantly, by modeling the distribution of all images via a graph, we can potentially reduce registration error since every time each image is warped only according to its nearby images with similar structures in the graph. We have evaluated our proposed group wise registration method on both synthetic and real datasets, with comparison to the two state-of-the-art group wise registration methods. All experimental results show that our proposed method achieves the best performance in terms of registration accuracy and robustness.
- Published
- 2013
41. Inter-group image registration by hierarchical graph shrinkage
- Author
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Shihui Ying, Shu Liao, Guorong Wu, and Dinggang Shen
- Subjects
education.field_of_study ,business.industry ,Population ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Image registration ,Graph theory ,Article ,Image representation ,Computer Science::Computer Vision and Pattern Recognition ,Graph (abstract data type) ,Computer vision ,Artificial intelligence ,Diffeomorphism ,education ,business ,Shrinkage ,Mathematics - Abstract
In this paper, we propose a novel inter-group image registration method to register different groups of images (e.g., young and elderly brains) simultaneously. Specifically, we use a hierarchical two-level graph to model the distribution of entire images on the manifold, with intra-graph representing the image distribution in each group and the inter-graph describing the relationship between two groups. Then the procedure of inter-group registration is formulated as a dynamic evolution of graph shrinkage. The advantage of our method is that the topology of entire image distribution is explored to guide the image registration. In this way, each image coordinates with its neighboring images on the manifold to deform towards the population center, by following the deformation pathway simultaneously optimized within the graph. Our proposed method has been also compared with other state-of-the-art inter-group registration methods, where our method achieves better registration results in terms of registration accuracy and robustness.
- Published
- 2013
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