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学者姓名：陈光永
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Abstract ：
Intracranial hemorrhage poses a critical threat to patient survival, necessitating rapid intervention to prevent devastating outcomes. Traditional segmentation methods in computer-aided diagnosis face significant challenges due to the inherent variability of hemorrhage regions. Recent advancements in segmentation, powered by foundation models and innovative utilization of prior knowledge, have shown promise; however, existing methods predominantly rely on point or bounding box prompts, which often fail to account for the intricate variability inherent in hemorrhage presentations. To tackle this challenge, we propose a knowledge- prompted segment anything model (KP-SAM) that integrates the specialized knowledge of neurologists into the segmentation process. By collaborating with expert neurologist, our method captures the nuanced characteristics of hemorrhage regions, effectively augmenting the limitations of using only points or bounding boxes. Furthermore, we developed a diagnostic support system for intracranial hemorrhage at the Affiliated Hospital of Qingdao University. Leveraging concise semantic information provided by radiologists, our system facilitates rapid and accurate diagnostic support for clinicians. Experimental results demonstrate that our method achieves state-of-the-art performance in real-world segmentation tasks and significantly enhances diagnostic accuracy for neurologists. This advancement not only enhances diagnostic precision but also highlights the transformative potential of integrating diverse data modalities in medical applications.
Keyword ：
CT CT Foundational models Foundational models Intracranial hemorrhage Intracranial hemorrhage Medical image segmentation Medical image segmentation Segment anything Segment anything
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| GB/T 7714 | Nie, Tianzong , Chen, Feiyan , Su, Jiannan et al. Knowledge-prompted intracranial hemorrhage segmentation on brain computed tomography [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 271 . |
| MLA | Nie, Tianzong et al. "Knowledge-prompted intracranial hemorrhage segmentation on brain computed tomography" . | EXPERT SYSTEMS WITH APPLICATIONS 271 (2025) . |
| APA | Nie, Tianzong , Chen, Feiyan , Su, Jiannan , Chen, Guangyong , Gan, Min . Knowledge-prompted intracranial hemorrhage segmentation on brain computed tomography . | EXPERT SYSTEMS WITH APPLICATIONS , 2025 , 271 . |
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We propose an online learning algorithm tailored for a class of machine learning models within a separable stochastic approximation framework. The central idea of our approach is to exploit the inherent separability in many models, recognizing that certain parameters are easier to optimize than others. This paper focuses on models where some parameters exhibit linear characteristics, which are common in machine learning applications. In our proposed algorithm, the linear parameters are updated using the recursive least squares (RLS) algorithm, akin to a stochastic Newton method. Subsequently, based on these updated linear parameters, the nonlinear parameters are adjusted using the stochastic gradient method (SGD). This dual-update mechanism can be viewed as a stochastic approximation variant of block coordinate gradient descent, where one subset of parameters is optimized using a second-order method while the other is handled with a first-order approach. We establish the global convergence of our online algorithm for non-convex cases in terms of the expected violation of first-order optimality conditions. Numerical experiments demonstrate that our method achieves significantly faster initial convergence and produces more robust performance compared to other popular learning algorithms. Additionally, our algorithm exhibits reduced sensitivity to learning rates and outperforms the recently proposed slimTrain algorithm (Newman et al. 2022). For validation, the code has been made available on GitHub.
Keyword ：
Approximation algorithms Approximation algorithms Artificial neural networks Artificial neural networks Convergence Convergence Convex functions Convex functions Machine learning Machine learning Machine learning algorithms Machine learning algorithms Minimization Minimization Online learning Online learning Optimization Optimization recursive least squares recursive least squares stochastic approximation stochastic approximation Stochastic processes Stochastic processes Training Training variable projection variable projection
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| GB/T 7714 | Gan, Min , Su, Xiang-xiang , Chen, Guang-yong et al. Online Learning Under a Separable Stochastic Approximation Framework [J]. | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2025 , 47 (2) : 1317-1330 . |
| MLA | Gan, Min et al. "Online Learning Under a Separable Stochastic Approximation Framework" . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 47 . 2 (2025) : 1317-1330 . |
| APA | Gan, Min , Su, Xiang-xiang , Chen, Guang-yong , Chen, Jing , Chen, C. L. Philip . Online Learning Under a Separable Stochastic Approximation Framework . | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2025 , 47 (2) , 1317-1330 . |
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This paper delves into an in-depth exploration of the Variable Projection (VP) algorithm, a powerful tool for solving separable nonlinear optimization problems across multiple domains, including system identification, image processing, and machine learning. We first establish a theoretical framework to examine the effect of the approximate treatment of the coupling relationship among parameters on the local convergence of the VP algorithm and theoretically prove that the Kaufman's VP algorithm can achieve a similar convergence rate as the Golub & Pereyra's form. These studies fill the gap in the existing convergence theory analysis, and provide a solid foundation for understanding the mechanism of VP algorithm and broadening its application horizons. Furthermore, inspired by these theoretical insights, we design a refined VP algorithm, termed VPLR, to address separable nonlinear optimization problems with large residual. This algorithm enhances convergence performance by addressing the coupling relationship between parameters in separable models and continually refining the approximated Hessian matrix to counteract the influence of large residual. The effectiveness of this refined algorithm is corroborated through numerical experiments. © 2025 Elsevier Ltd
Keyword ：
Separable nonlinear optimization problem Separable nonlinear optimization problem System identification System identification Variable projection Variable projection
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| GB/T 7714 | Chen, G. , Xue, P. , Gan, M. et al. Variable Projection algorithms: Theoretical insights and a novel approach for problems with large residual [J]. | Automatica , 2025 , 177 . |
| MLA | Chen, G. et al. "Variable Projection algorithms: Theoretical insights and a novel approach for problems with large residual" . | Automatica 177 (2025) . |
| APA | Chen, G. , Xue, P. , Gan, M. , Chen, J. , Guo, W. , Chen, C.L.P. . Variable Projection algorithms: Theoretical insights and a novel approach for problems with large residual . | Automatica , 2025 , 177 . |
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Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and a nonlinear part. Inspired by the high efficiency of variable projection algorithm for solving separable nonlinear least squares problems, in this paper, we develop a robust variable projection (RoVP) method for the parameter estimation of separable nonlinear regression problem with $L_{1}$ norm loss. The proposed algorithm eliminates the linear parameters by solving a linear programming subproblem, resulting in a reduced problem that only involves nonlinear parameters. More importantly, we derive the Jacobian matrix of the reduced objective function, which tackles the coupling between the linear parameters and nonlinear parameters. Furthermore, we observed an intriguing phenomenon in the landscape of the original separable nonlinear objective function, where some narrow valleys frequently exist. The RoVP strategy can effectively diminish the likelihood of the algorithm getting stuck in these valleys and accelerate its convergence. Numerical experiments confirm the effectiveness and robustness of the proposed algorithm. IEEE
Keyword ：
Autoregressive processes Autoregressive processes Jacobian matrices Jacobian matrices Linear programming Linear programming Optimization Optimization Parameter estimation Parameter estimation Predictive models Predictive models radial basis function network based autoregressive (RBF-AR) model radial basis function network based autoregressive (RBF-AR) model robust parameter estimation robust parameter estimation Signal processing algorithms Signal processing algorithms System identification System identification variable projection (VP) algorithm variable projection (VP) algorithm
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| GB/T 7714 | Chen, G. , Su, X. , Gan, M. et al. Robust variable projection algorithm for the identification of separable nonlinear models [J]. | IEEE Transactions on Automatic Control , 2024 , 69 (9) : 1-8 . |
| MLA | Chen, G. et al. "Robust variable projection algorithm for the identification of separable nonlinear models" . | IEEE Transactions on Automatic Control 69 . 9 (2024) : 1-8 . |
| APA | Chen, G. , Su, X. , Gan, M. , Guo, W. , Chen, C.L.P. . Robust variable projection algorithm for the identification of separable nonlinear models . | IEEE Transactions on Automatic Control , 2024 , 69 (9) , 1-8 . |
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Non-local attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single-image super-resolution (SR). However, NLA suffers from high computational complexity and memory consumption, as it requires aggregating all non-local feature information for each query response and recalculating the similarity weight distribution for different abstraction levels of features. To address these challenges, we propose a novel learnable collaborative attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA consists of two components: learnable sparse pattern (LSP) and collaborative attention (CoA). LSP uses the k-means clustering algorithm to dynamically adjust the sparse attention pattern of deep features, which reduces the number of non-local modeling rounds compared with existing sparse solutions. CoA leverages the sparse attention pattern and weights learned by LSP and co-optimizes the similarity matrix across different abstraction levels, which avoids redundant similarity matrix calculations. The experimental results show that our LCoA can reduce the non-local modeling time by about 83% in the inference stage. In addition, we integrate our LCoA into a deep learnable collaborative attention network (LCoAN), which achieves competitive performance in terms of inference time, memory consumption, and reconstruction quality compared with other state-of-the-art SR methods. Our code and pre-trained LCoAN models were uploaded to GitHub (https://github.com/YigangZhao/LCoAN) for validation. © 2024 SPIE and IS&T.
Keyword ：
k-means clustering k-means clustering non-local attention non-local attention self-similarity self-similarity single-image super-resolution single-image super-resolution
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| GB/T 7714 | Zhao, Y. , Zheng, C. , Su, J. et al. Efficient learnable collaborative attention for single-image super-resolution [J]. | Journal of Electronic Imaging , 2024 , 33 (6) . |
| MLA | Zhao, Y. et al. "Efficient learnable collaborative attention for single-image super-resolution" . | Journal of Electronic Imaging 33 . 6 (2024) . |
| APA | Zhao, Y. , Zheng, C. , Su, J. , Chen, G. . Efficient learnable collaborative attention for single-image super-resolution . | Journal of Electronic Imaging , 2024 , 33 (6) . |
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Separable nonlinear models (SNLMs) are of great importance in system modeling, signal processing, and machine learning because of their flexible structure and excellent description of nonlinear behaviors. The online identification of such models is quite challenging, and previous related work usually ignores the special structure where the estimated parameters can be partitioned into a linear and a nonlinear part. In this brief, we propose an efficient first-order recursive algorithm for SNLMs by introducing the variable projection (VP) step. The proposed algorithm utilizes the recursive least-squares method to eliminate the linear parameters, resulting in a reduced function. Then, the stochastic gradient descent (SGD) algorithm is employed to update the parameters of the reduced function. By considering the tight coupling relationship between linear parameters and nonlinear parameters, the proposed first-order VP algorithm is more efficient and robust than the traditional SGD algorithm and alternating optimization algorithm. More importantly, since the proposed algorithm just uses the first-order information, it is easier to apply it to large-scale models. Numerical results on examples of different sizes confirm the effectiveness and efficiency of the proposed algorithm. © 2012 IEEE.
Keyword ：
Couplings Couplings E-learning E-learning Flexible structures Flexible structures Learning algorithms Learning algorithms Learning systems Learning systems Least squares approximations Least squares approximations Machine learning Machine learning Nonlinear systems Nonlinear systems Numerical methods Numerical methods Online systems Online systems Optimization Optimization Parameter estimation Parameter estimation Random processes Random processes Stochastic models Stochastic models Stochastic systems Stochastic systems
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| GB/T 7714 | Chen, Guang-Yong , Gan, Min , Chen, Long et al. Online Identification of Nonlinear Systems With Separable Structure [J]. | IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 (6) : 8695-8701 . |
| MLA | Chen, Guang-Yong et al. "Online Identification of Nonlinear Systems With Separable Structure" . | IEEE Transactions on Neural Networks and Learning Systems 35 . 6 (2024) : 8695-8701 . |
| APA | Chen, Guang-Yong , Gan, Min , Chen, Long , Chen, C. L. Philip . Online Identification of Nonlinear Systems With Separable Structure . | IEEE Transactions on Neural Networks and Learning Systems , 2024 , 35 (6) , 8695-8701 . |
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In recent years, convolutional neural networks have excelled in image Moiré pattern removal, yet their high memory consumption poses challenges for resource-constrained devices. To address this, we propose the lightweight multi-scale network (LMSNet). Designing lightweight multi-scale feature extraction blocks and efficient adaptive channel fusion modules, we extend the receptive field of feature extraction and introduce lightweight convolutional decomposition. LMSNet achieves a balance between parameter numbers and reconstruction performance. Extensive experiments demonstrate that our LMSNet, with 0.77 million parameters, achieves Moiré pattern removal performance comparable to full high definition demoiréing network (FHDe2Net) with 13.57 million parameters. © 2024 SPIE and IS&T.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Distillation Distillation Extraction Extraction Feature extraction Feature extraction Image reconstruction Image reconstruction
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| GB/T 7714 | Li, XuWen , Gan, Min , Su, JianNan et al. Efficient Moiré pattern removal with lightweight multi-scale feature extraction [J]. | Journal of Electronic Imaging , 2024 , 33 (2) . |
| MLA | Li, XuWen et al. "Efficient Moiré pattern removal with lightweight multi-scale feature extraction" . | Journal of Electronic Imaging 33 . 2 (2024) . |
| APA | Li, XuWen , Gan, Min , Su, JianNan , Chen, GuangYong . Efficient Moiré pattern removal with lightweight multi-scale feature extraction . | Journal of Electronic Imaging , 2024 , 33 (2) . |
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Underwater image enhancement, especially in color restoration and detail reconstruction, remains a significant challenge. Current models focus on improving accuracy and learning efficiency through neural network design, often neglecting traditional optimization algorithms’ benefits. We propose FAIN-UIE, a novel approach for color and fine-texture recovery in underwater imagery. It leverages insights from the Fast Iterative Shrink-Threshold Algorithm (FISTA) to approximate image degradation, enhancing network fitting speed. FAIN-UIE integrates the residual degradation module (RDM) and momentum calculation module (MC) for gradient descent and momentum simulation, addressing feature fusion losses with the Feature Merge Block (FMB). By integrating multi-scale information and inter-stage pathways, our method effectively maps multi-stage image features, advancing color and fine-texture restoration. Experimental results validate its robust performance, positioning FAIN-UIE as a competitive solution for practical underwater imaging applications. © 2024 Elsevier Inc.
Keyword ：
Deep learning Deep learning FISTA algorithm FISTA algorithm Proximal gradient Proximal gradient Underwater image enhancement Underwater image enhancement
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| GB/T 7714 | Chen, B.-Y. , Su, J.-N. , Chen, G.-Y. et al. FISTA acceleration inspired network design for underwater image enhancement [J]. | Journal of Visual Communication and Image Representation , 2024 , 103 . |
| MLA | Chen, B.-Y. et al. "FISTA acceleration inspired network design for underwater image enhancement" . | Journal of Visual Communication and Image Representation 103 (2024) . |
| APA | Chen, B.-Y. , Su, J.-N. , Chen, G.-Y. , Gan, M. . FISTA acceleration inspired network design for underwater image enhancement . | Journal of Visual Communication and Image Representation , 2024 , 103 . |
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The graph-information-based fuzzy clustering has shown promising results in various datasets. However, its performance is hindered when dealing with high-dimensional data due to challenges related to redundant information and sensitivity to the similarity matrix design. To address these limitations, this article proposes an implicit fuzzy k-means (FKMs) model that enhances graph-based fuzzy clustering for high-dimensional data. Instead of explicitly designing a similarity matrix, our approach leverages the fuzzy partition result obtained from the implicit FKMs model to generate an effective similarity matrix. We employ a projection-based technique to handle redundant information, eliminating the need for specific feature extraction methods. By formulating the fuzzy clustering model solely based on the similarity matrix derived from the membership matrix, we mitigate issues, such as dependence on initial values and random fluctuations in clustering results. This innovative approach significantly improves the competitiveness of graph-enhanced fuzzy clustering for high-dimensional data. We present an efficient iterative optimization algorithm for our model and demonstrate its effectiveness through theoretical analysis and experimental comparisons with other state-of-the-art methods, showcasing its superior performance.
Keyword ：
Computational modeling Computational modeling Data models Data models Feature extraction Feature extraction Fuzzy clustering Fuzzy clustering graph clustering graph clustering high-dimensional data high-dimensional data High-dimensional data High-dimensional data Image segmentation Image segmentation implicit model implicit model Manifolds Manifolds Optimization Optimization
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| GB/T 7714 | Shi, Zhaoyin , Chen, Long , Ding, Weiping et al. IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2024 . |
| MLA | Shi, Zhaoyin et al. "IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering" . | IEEE TRANSACTIONS ON CYBERNETICS (2024) . |
| APA | Shi, Zhaoyin , Chen, Long , Ding, Weiping , Zhong, Xiaopin , Wu, Zongze , Chen, Guang-Yong et al. IFKMHC: Implicit Fuzzy K-Means Model for High-Dimensional Data Clustering . | IEEE TRANSACTIONS ON CYBERNETICS , 2024 . |
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Robust nonlinear regression frequently arises in data analysis that is affected by outliers in various application fields such as system identification, signal processing, and machine learning. However, it is still quite challenge to design an efficient algorithm for such problems due to the nonlinearity and nonsmoothness. Previous researches usually ignore the underlying structure presenting in the such nonlinear regression models, where the variables can be partitioned into a linear part and a nonlinear part. Inspired by the high efficiency of variable projection algorithm for solving separable nonlinear least squares problems, in this article, we develop a robust variable projection (RoVP) method for the parameter estimation of separable nonlinear regression problem with L-1 norm loss. The proposed algorithm eliminates the linear parameters by solving a linear programming subproblem, resulting in a reduced problem that only involves nonlinear parameters. More importantly, we derive the Jacobian matrix of the reduced objective function, which tackles the coupling between the linear parameters and nonlinear parameters. Furthermore, we observed an intriguing phenomenon in the landscape of the original separable nonlinear objective function, where some narrow valleys frequently exist. The RoVP strategy can effectively diminish the likelihood of the algorithm getting stuck in these valleys and accelerate its convergence. Numerical experiments confirm the effectiveness and robustness of the proposed algorithm.
Keyword ：
Autoregressive processes Autoregressive processes Jacobian matrices Jacobian matrices Linear programming Linear programming Optimization Optimization Parameter estimation Parameter estimation Predictive models Predictive models Radial basis function network based autoregressive (RBF-AR) model Radial basis function network based autoregressive (RBF-AR) model robust parameter estimation robust parameter estimation Signal processing algorithms Signal processing algorithms system identification system identification variable projection (VP) algorithm variable projection (VP) algorithm
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| GB/T 7714 | Chen, Guang-Yong , Su, Xiang-Xiang , Gan, Min et al. Robust Variable Projection Algorithm for the Identification of Separable Nonlinear Models [J]. | IEEE TRANSACTIONS ON AUTOMATIC CONTROL , 2024 , 69 (9) : 6293-6300 . |
| MLA | Chen, Guang-Yong et al. "Robust Variable Projection Algorithm for the Identification of Separable Nonlinear Models" . | IEEE TRANSACTIONS ON AUTOMATIC CONTROL 69 . 9 (2024) : 6293-6300 . |
| APA | Chen, Guang-Yong , Su, Xiang-Xiang , Gan, Min , Guo, Wenzhong , Chen, C. L. Philip . Robust Variable Projection Algorithm for the Identification of Separable Nonlinear Models . | IEEE TRANSACTIONS ON AUTOMATIC CONTROL , 2024 , 69 (9) , 6293-6300 . |
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