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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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Retinex-based methods have become a general approach for solving low-light image enhancement (LLIE). However, traditional methods require post-processing of illumination (e.g., gamma correction), which lacks adaptability and disrupts the illumination structure. Retinex-based deep networks typically follow a ‘decomposition-adjustment-exposure control’ process, which is redundant and lacks robustness. One major issue is the inaccuracy in estimating and decomposing the initial illumination. Accurate initial illumination can prevent further post-processing instability. We propose IniRetinex, rethinking the Retinex-based LLIE method from the perspective of initialization. By using neural networks to provide reasonable initial illumination and solving for smooth illumination through optimization, higher performance LLIE is achieved. We construct a two-layer convolutional neural network to capture the low-frequency structure of the image, adaptively compensating for classical initial illumination and avoiding additional post-processing. The network requires no pre-training and can be implemented in an unsupervised manner with just a few iterations, making it highly efficient. Additionally, we propose a new illumination optimization strategy by introducing an additional proximal penalty term, improving illumination in areas with varying levels and enhancing image details. Extensive experiments on various low-light image datasets demonstrate that our method achieves state-of-the-art (SOTA) results on multiple benchmarks, offering higher stability and inference efficiency compared to current advanced methods. © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Keyword ：
Convolutional neural networks Convolutional neural networks Deep neural networks Deep neural networks Photointerpretation Photointerpretation
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| GB/T 7714 | Fan, Guodong , Yao, Zishu , Chen, Guang-Yong et al. IniRetinex: Rethinking Retinex-type Low-Light Image Enhancer via Initialization Perspective [C] . 2025 : 2834-2842 . |
| MLA | Fan, Guodong et al. "IniRetinex: Rethinking Retinex-type Low-Light Image Enhancer via Initialization Perspective" . (2025) : 2834-2842 . |
| APA | Fan, Guodong , Yao, Zishu , Chen, Guang-Yong , Su, Jian-Nan , Gan, Min . IniRetinex: Rethinking Retinex-type Low-Light Image Enhancer via Initialization Perspective . (2025) : 2834-2842 . |
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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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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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Sparse coding based domain adaptation methods aim to learn a robust transfer classifier by utilizing the knowledge from source domain and the learned new representation of both domains. Most existing works have achieved remarkable results in solving linear domain shift problems, but have poor performance in nonlinear domain shift problems. In this paper, we propose a Transfer kernel sparse coding based on dynamic distribution alignment (TKSC-DDA) approach for cross-domain visual recognition, which incorporates dynamic distributed alignment into kernel sparse coding to learn discriminative and robust sparse representations. Extensive experiment on visual transfer learning tasks demonstrate that our proposed method can significantly out-perform serval state-of-the-art approaches. © 2024 IEEE.
Keyword ：
Alignment Alignment Computer vision Computer vision Image representation Image representation Learning systems Learning systems
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| GB/T 7714 | Huang, Wei , Gan, Min , Chen, Guangyong . Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation [C] . 2024 : 230-234 . |
| MLA | Huang, Wei et al. "Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation" . (2024) : 230-234 . |
| APA | Huang, Wei , Gan, Min , Chen, Guangyong . Transfer Kernel Sparse Coding Based on Dynamic Distribution Alignment for Image Representation . (2024) : 230-234 . |
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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.
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, Bing-Yuan , Su, Jian-Nan , Chen, Guang-Yong et al. FISTA acceleration inspired network design for underwater image enhancement [J]. | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION , 2024 , 103 . |
| MLA | Chen, Bing-Yuan et al. "FISTA acceleration inspired network design for underwater image enhancement" . | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 103 (2024) . |
| APA | Chen, Bing-Yuan , Su, Jian-Nan , Chen, Guang-Yong , Gan, Min . FISTA acceleration inspired network design for underwater image enhancement . | JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION , 2024 , 103 . |
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In recent years, convolutional neural networks have excelled in image Moir & eacute; 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 Moir & eacute; pattern removal performance comparable to full high definition demoir & eacute;ing network (FHDe(2)Net) with 13.57 million parameters.
Keyword ：
image demoir & eacute;ing image demoir & eacute;ing image restoration image restoration information multi-distillation information multi-distillation lightweight network lightweight network multi-scale feature extraction and fusion multi-scale feature extraction and fusion
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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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Separable nonlinear models (SNMs) serve as potent instruments for system identification, data analysis, and machine learning. However, the online identification of SNMs poses a greater challenge compared to their offline counterparts, primarily due to the dynamic nature of nonlinear systems. Traditional approaches, including the recursive Gauss-Newton and recursive variable projection methods, falter in managing parameter interdependence, culminating in sluggish convergence and suboptimal outcomes. Addressing these limitations, our study introduces a pioneering multi-innovation-based recursive variabl projection (MIRVP) algorithm, which extends the RVP algorithm with a multi-innovation strategy. This strategy enables the algorithm to handle the interaction of the linear and nonlinear parameters more effectively, by using multiple past innovations instead of only the current one, thus improving the identification effect. The proposed algorithm's effectiveness has been validated through tests on synthetic data, real-life industrial control tracking scenarios such as the Box-Jenkins gas furnace data and Glass tube drawing processing, as well as on the training of large-scale neural networks. The results demonstrate that our algorithm outperforms existing methods in terms of convergence speed and robustness
Keyword ：
Multi-innovation method Multi-innovation method Online identification Online identification Recursive Gauss-Newton method Recursive Gauss-Newton method Separable nonlinear modeling Separable nonlinear modeling
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| GB/T 7714 | Su, Jian-Nan , Su, Xiang-Xiang , Chen, Guang-Yong et al. Multi-innovation-based online variable projection algorithm for a class of nonlinear models [J]. | NONLINEAR DYNAMICS , 2024 , 112 (16) : 14107-14122 . |
| MLA | Su, Jian-Nan et al. "Multi-innovation-based online variable projection algorithm for a class of nonlinear models" . | NONLINEAR DYNAMICS 112 . 16 (2024) : 14107-14122 . |
| APA | Su, Jian-Nan , Su, Xiang-Xiang , Chen, Guang-Yong , Gan, Min , Chen, C. L. Philip . Multi-innovation-based online variable projection algorithm for a class of nonlinear models . | NONLINEAR DYNAMICS , 2024 , 112 (16) , 14107-14122 . |
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Separable nonlinear models are pervasively employed in diverse disciplines, such as system identification, signal analysis, electrical engineering, and machine learning. Identifying these models inherently poses a non-convex optimization challenge. While gradient descent (GD) is a commonly adopted method, it is often plagued by suboptimal convergence rates and is highly dependent on the appropriate choice of step size. To mitigate these issues, we introduce an augmented GD algorithm enhanced with Anderson acceleration (AA), and propose a Hierarchical GD with Anderson acceleration (H-AAGD) method for efficient identification of separable nonlinear models. This novel approach transcends the conventional step size constraints of GD algorithms and considers the coupling relationships between different parameters during the optimization process, thereby enhancing the efficiency of the solution-finding process. Unlike the Newton method, our algorithm obviates the need for computing the inverse of the Hessian matrix, simplifying the computational process. Additionally, we theoretically analyze the convergence and complexity of the algorithm and validate its effectiveness through a series of numerical experiments.
Keyword ：
Anderson acceleration Anderson acceleration Hierarchical identification algorithm Hierarchical identification algorithm Robust parameter estimation Robust parameter estimation Separable nonlinear problem Separable nonlinear problem
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| GB/T 7714 | Chen, Guang-Yong , Lin, Xin , Xue, Peng et al. Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models [J]. | NONLINEAR DYNAMICS , 2024 , 113 (10) : 11371-11387 . |
| MLA | Chen, Guang-Yong et al. "Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models" . | NONLINEAR DYNAMICS 113 . 10 (2024) : 11371-11387 . |
| APA | Chen, Guang-Yong , Lin, Xin , Xue, Peng , Gan, Min . Efficient gradient descent algorithm with anderson acceleration for separable nonlinear models . | NONLINEAR DYNAMICS , 2024 , 113 (10) , 11371-11387 . |
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