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学者姓名：陈飞
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Node classification tasks have seen considerable progress with the use of Graph Neural Networks (GNNs). However, they cannot work well on imbalanced node classification and tend to prioritize the majority classes with more labeled instances while overlooking the minority classes with fewer labeled instances. Existing solutions focus on generating new nodes to augment the training set, which may disrupt the original topological structure of the graph, so GNNs may not achieve optimal classification results. To address this issue, we introduce GraphMMC, a reliable and flexible strategy to generate pseudo-labels that can be easily integrated with various GNNs, which will augment the imbalance training set to a class-balanced set without generating new nodes. We use the similarity between the unlabeled nodes and the minority classes to correction the low-confidence pseudo-labels generated by GNNs to obtain reliable pseudo-labels. Our experiments demonstrate that the proposed method outperforms state-of-the-art baselines on several class-imbalanced datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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| GB/T 7714 | Ma, J. , Chen, F. , Jiang, F. et al. GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification [未知]. |
| MLA | Ma, J. et al. "GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification" [未知]. |
| APA | Ma, J. , Chen, F. , Jiang, F. , Cheng, H. , Wang, M. . GraphMMC: Class-Balanced Pseudo-Labels Generation for Graph Node Classification [未知]. |
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This paper investigates the problem of circular structure detection, which is of great significance in medical detection scenarios. Existing detection methods usually treat this problem as a special case of object detection and adapt existing object detectors. However, these methods ignore the inherent properties of graphs, leading to poor model-checking performance and weak generalization ability. Compared with complex shape targets, the geometric properties of circles are simpler, so the graphics parameters can be found in the complex parameter space through specific feature extractors for shape detection. This paper presents an end-to-end deep learning framework for circular object detection in medical images. Leveraging deep features extracted by convolutional neural networks (CNNs), the proposed method integrates a dynamically differentiable Hough transform module to effectively map image-domain features to the parameter space of circles, defined by their center coordinates and radii. This parameter space's aggregation and projection mechanism concentrates features onto potential circular trajectories, generating distinct peak responses. The proposed approach achieves an efficient and fully differentiable detection pipeline by transforming object detection into a peak localization task, eliminating the need for traditional non-maximum suppression (NMS). In addition, this paper also uses the circular IoU loss function for circular objects. The experiment shows that the dynamic deep circular Hough transform and circular IoU loss function have better detection effects on medical circular objects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
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| GB/T 7714 | Liu, W. , Lu, W. , Huang, Z. et al. DCHT-Net: Medical Object Detection Based on Dynamic Deep Circular Hough Transform [未知]. |
| MLA | Liu, W. et al. "DCHT-Net: Medical Object Detection Based on Dynamic Deep Circular Hough Transform" [未知]. |
| APA | Liu, W. , Lu, W. , Huang, Z. , Huang, B. , Chen, F. . DCHT-Net: Medical Object Detection Based on Dynamic Deep Circular Hough Transform [未知]. |
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Trajectory reconstruction aims to recover the missing position of moving objects in a given video, which is important for predicting the behavior of motion objects. The existing trajectory estimations focus on frame-by-frame object detection and tracking. However, these methods cannot handle complex situations such as mutual occlusion of multiple objects. To solve the posed problem, we propose a novel motion trajectory reconstruction based on object matching and graph signal processing. Specifically, we first extract the features of objects by detection and then perform object matching via similarity scores. The gradient graph Laplacian regularizer is used to interpolate the missing trajectory signals. We can effectively reconstruct motion trajectory signals with a higher sampling ratio of video frames. We do not require knowledge of the underlying motion information and can accurately estimate the position in occlusion. Experimental results show that the quality of our trajectory reconstruction outperforms other competing algorithms.
Keyword ：
Gradient Graph Laplacian Regularizer Gradient Graph Laplacian Regularizer Graph Signal Processing Graph Signal Processing Trajectory Reconstruction Trajectory Reconstruction
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| GB/T 7714 | Zhang, Siping , Chen, Fei , Liu, Wanling et al. Motion Trajectory Reconstruction Based on Feature Matching and Gradient Graph Laplacian Regularizer [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT X , 2025 , 15040 : 313-326 . |
| MLA | Zhang, Siping et al. "Motion Trajectory Reconstruction Based on Feature Matching and Gradient Graph Laplacian Regularizer" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT X 15040 (2025) : 313-326 . |
| APA | Zhang, Siping , Chen, Fei , Liu, Wanling , Chen, Huayi , Zeng, Xunxun . Motion Trajectory Reconstruction Based on Feature Matching and Gradient Graph Laplacian Regularizer . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT X , 2025 , 15040 , 313-326 . |
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Due to the cost and accuracy of current point cloud sampling equipment, the obtained point color information is often corrupted by various noises. Existing point cloud denoising algorithms mainly focus on smoothness priors and convex optimization. Their performances highly depend on model parameters whose values are determined manually and fixed throughout the iterations. In this paper, we propose to unfold gradient graph regularization with deep neural networks for point cloud color denoising. It improves the robustness of the model for denoising in different kinds of datasets and across domains. Specifically, our approach first uses a point cloud extraction network to obtain effective features for gradient computation. Then, we construct a gradient graph Laplacian regularization (GGLR) as signal smoothness prior to point cloud restoration. Finally, we introduce shallow neural networks for model parameter estimation to unfold GGLR. The proposed point cloud denoising framework is fully differentiable and can be trained end-to-end. Experiments show that the proposed algorithm unfolding outperforms several existing point cloud color denoising techniques.
Keyword ：
Algorithm unfolding Algorithm unfolding Color denoising Color denoising Graph signal processing Graph signal processing Point cloud Point cloud
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| GB/T 7714 | Wang, Hongtao , Chen, Fei , Liu, Wanling et al. Unfolding Gradient Graph Regularization for Point Cloud Color Denoising [J]. | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT VI , 2025 , 15036 : 565-579 . |
| MLA | Wang, Hongtao et al. "Unfolding Gradient Graph Regularization for Point Cloud Color Denoising" . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT VI 15036 (2025) : 565-579 . |
| APA | Wang, Hongtao , Chen, Fei , Liu, Wanling , Zeng, Xunxun . Unfolding Gradient Graph Regularization for Point Cloud Color Denoising . | PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2024, PT VI , 2025 , 15036 , 565-579 . |
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Image manipulation has sparked widespread concern due to its potential security threats on the Internet. The boundary between the authentic and manipulated region exhibits artifacts in image manipulation localization (IML). These artifacts are more pronounced in heterogeneous image splicing and homogeneous image copy-move manipulation, while they are more subtle in removal and inpainting manipulated images. However, existing methods for image manipulation detection tend to capture boundary artifacts via explicit edge features and have limitations in effectively addressing subtle artifacts. Besides, feature redundancy caused by the powerful feature extraction capability of large models may prevent accurate identification of manipulated artifacts, exhibiting a high false-positive rate. To solve these problems, we propose a novel edge-aware network (EAN) to capture boundary artifacts effectively. This network treats the image manipulation localization problem as a segmentation problem inside and outside the boundary. In EAN, we develop an edge-aware mechanism to refine implicit and explicit edge features by the interaction of adjacent features. This approach directs the encoder to prioritize the desired edge information. Also, we design a multi-feature fusion strategy combined with an improved attention mechanism to enhance key feature representation significantly for mitigating the effects of feature redundancy. We perform thorough experiments on diverse datasets, and the outcomes confirm the efficacy of the suggested approach, surpassing leading manipulation localization techniques in the majority of scenarios.
Keyword ：
attention mechanism attention mechanism Attention mechanisms Attention mechanisms convolutional neural network convolutional neural network Discrete wavelet transforms Discrete wavelet transforms Feature extraction Feature extraction feature fusion feature fusion Image edge detection Image edge detection Image manipulation localization Image manipulation localization Location awareness Location awareness Neural networks Neural networks Noise Noise Semantics Semantics Splicing Splicing Transformers Transformers
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| GB/T 7714 | Chen, Yun , Cheng, Hang , Wang, Haichou et al. EAN: Edge-Aware Network for Image Manipulation Localization [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) : 1591-1601 . |
| MLA | Chen, Yun et al. "EAN: Edge-Aware Network for Image Manipulation Localization" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 35 . 2 (2025) : 1591-1601 . |
| APA | Chen, Yun , Cheng, Hang , Wang, Haichou , Liu, Ximeng , Chen, Fei , Li, Fengyong et al. EAN: Edge-Aware Network for Image Manipulation Localization . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2025 , 35 (2) , 1591-1601 . |
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Image hiding through the application of invertible neural network (INN) represents a significant branch within the realm of deep image hiding methodologies, characterized by a compact network architecture and a streamlined parameter count. Nonetheless, when juxtaposed with autoencoder-based approaches, existing INN methods often result in inferior image quality. To surmount this challenge, this paper introduces a novel masking-based image hiding paradigm, establishes a new spatial domain transformation for images, and refines the Swin-transformer block. By integrating these innovations, an INN architecture is crafted that is particularly adept for deep image hiding, termed NiNet. The experimental results demonstrate that NiNet can remarkably address the problem of image hiding. In terms of steganographic image quality, NiNet outperforms the current state-of-the-art (SOTA) algorithms by 0.26 dB on the DIV2K dataset, 1.49 dB on the COCO dataset, and 0.39 dB on the ImageNet dataset. Regarding the quality of secret image recovery, NiNet surpasses the SOTA algorithms by 2.06 dB on DIV2K, 1.98 dB on COCO, and 0.50 dB on ImageNet.
Keyword ：
Covert communication Covert communication Image hiding Image hiding Information security Information security Invertible neural networks Invertible neural networks Spatial domain transformation Spatial domain transformation
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| GB/T 7714 | Ni, Zishun , Cheng, Hang , Chen, Jiaoling et al. NiNet: A new invertible neural network architecture more suitable for deep image hiding [J]. | INFORMATION PROCESSING & MANAGEMENT , 2025 , 62 (6) . |
| MLA | Ni, Zishun et al. "NiNet: A new invertible neural network architecture more suitable for deep image hiding" . | INFORMATION PROCESSING & MANAGEMENT 62 . 6 (2025) . |
| APA | Ni, Zishun , Cheng, Hang , Chen, Jiaoling , Xu, Yongliang , Chen, Fei , Wang, Meiqing . NiNet: A new invertible neural network architecture more suitable for deep image hiding . | INFORMATION PROCESSING & MANAGEMENT , 2025 , 62 (6) . |
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While deep learning techniques, such as Convolutional neural networks (CNNs), show significant potential in medical applications, real-time detection of parathyroid glands (PGs) during complex surgeries remains insufficiently explored, posing challenges for surgical accuracy and outcomes. Previous studies highlight the importance of leveraging prior knowledge, such as shape, for feature extraction in detection tasks. However, they fail to address the critical multi-scale variability of PG objects, resulting in suboptimal performance and efficiency. In this paper, we propose an end-to-end framework, MSWF-PGD, for Multi-Scale Weighted Fusion Parathyroid Gland Detection. To improve accuracy and efficiency, our approach extracts feature maps from convolutional layers at multiple scales and re-weights them using cluster-aware multi-scale alignment, considering diverse attributes such as the size, color, and position of PGs. Additionally, we introduce Multi-Scale Aggregation to enhance scale interactions and enable adaptive multi-scale feature fusion, providing precise and informative locality information for detection. Extensive comparative experiments and ablation studies on the parathyroid dataset (PGsdata) demonstrate the proposed framework's superiority in accuracy and real-time efficiency, outperforming state-of-the-art models such as RetinaNet, FCOS, and YOLOv8.
Keyword ：
feature fusion feature fusion multi-scale features multi-scale features object detection object detection parathyroid glands parathyroid glands prior information prior information
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| GB/T 7714 | Liu, Wanling , Lu, Wenhuan , Li, Yijian et al. Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism [J]. | ELECTRONICS , 2025 , 14 (6) . |
| MLA | Liu, Wanling et al. "Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism" . | ELECTRONICS 14 . 6 (2025) . |
| APA | Liu, Wanling , Lu, Wenhuan , Li, Yijian , Chen, Fei , Jiang, Fan , Wei, Jianguo et al. Parathyroid Gland Detection Based on Multi-Scale Weighted Fusion Attention Mechanism . | ELECTRONICS , 2025 , 14 (6) . |
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In the graph signal processing (GSP) literature, graph Laplacian regularizer (GLR) was used for signal restoration to promote piecewise smooth / constant reconstruction with respect to an underlying graph. However, for signals slowly varying across graph kernels, GLR suffers from an undesirable "staircase" effect. In this paper, focusing on manifold graphs-collections of uniform discrete samples on low-dimensional continuous manifolds-we generalize GLR to gradient graph Laplacian regularizer (GGLR) that promotes planar / piecewise planar (PWP) signal reconstruction. Specifically, for a graph endowed with sampling coordinates (e.g., 2D images, 3D point clouds), we first define a gradient operator, using which we construct a gradient graph for nodes' gradients in the sampling manifold space. This maps to a gradient-induced nodal graph (GNG) and a positive semi-definite (PSD) Laplacian matrix with planar signals as the 0 frequencies. For manifold graphs without explicit sampling coordinates, we propose a graph embedding method to obtain node coordinates via fast eigenvector computation. We derive the means-square-error minimizing weight parameter for GGLR efficiently, trading off bias and variance of the signal estimate. Experimental results show that GGLR outperformed previous graph signal priors like GLR and graph total variation (GTV) in a range of graph signal restoration tasks.
Keyword ：
graph embedding graph embedding Graph signal processing Graph signal processing graph smoothness priors graph smoothness priors quadratic programming quadratic programming
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| GB/T 7714 | Chen, Fei , Cheung, Gene , Zhang, Xue . Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer [J]. | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2024 , 72 : 744-761 . |
| MLA | Chen, Fei et al. "Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer" . | IEEE TRANSACTIONS ON SIGNAL PROCESSING 72 (2024) : 744-761 . |
| APA | Chen, Fei , Cheung, Gene , Zhang, Xue . Manifold Graph Signal Restoration Using Gradient Graph Laplacian Regularizer . | IEEE TRANSACTIONS ON SIGNAL PROCESSING , 2024 , 72 , 744-761 . |
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Image steganography attempts to imperceptibly hide the secret image within the cover image. Most of the existing deep learning -based steganography approaches have excelled in payload capacity, visual quality, and steganographic security. However, they are difficult to losslessly reconstruct secret images from stego images with relatively large payload capacity. Recently, although some studies have introduced invertible neural networks (INNs) to achieve largecapacity image steganography, these methods still cannot reconstruct the secret image losslessly due to the existence of lost information on the output side of the concealing network. We present an INN -based framework in this paper for lossless image steganography. Specifically, we regard image steganography as an image super -resolution task that converts low -resolution cover images to high -resolution stego images while hiding secret images. The feature dimension of the generated stego image matches the total dimension of the input secret and cover images, thereby eliminating the lost information. Besides, a bijective secret projection module is designed to transform various secret images into a latent variable that follows a simple distribution, improving the imperceptibility of the secret image. Comprehensive experiments indicate that the proposed framework achieves secure hiding and lossless extraction of the secret image.
Keyword ：
Covert communication Covert communication Information security Information security Invertible neural networks Invertible neural networks Lossless steganography Lossless steganography
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| GB/T 7714 | Wang, Tingqiang , Cheng, Hang , Liu, Ximeng et al. Lossless image steganography: Regard steganography as super-resolution [J]. | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (4) . |
| MLA | Wang, Tingqiang et al. "Lossless image steganography: Regard steganography as super-resolution" . | INFORMATION PROCESSING & MANAGEMENT 61 . 4 (2024) . |
| APA | Wang, Tingqiang , Cheng, Hang , Liu, Ximeng , Xu, Yongliang , Chen, Fei , Wang, Meiqing et al. Lossless image steganography: Regard steganography as super-resolution . | INFORMATION PROCESSING & MANAGEMENT , 2024 , 61 (4) . |
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The health-related Internet of Things (IoT) plays an irreplaceable role in the collection, analysis, and transmission of medical data. As a device of the health-related IoT, the electroencephalogram (EEG) has long been a powerful tool for physiological and clinical brain research, which contains a wealth of personal information. Due to its rich computational/storage resources, cloud computing is a promising solution to extract the sophisticated feature of massive EEG signals in the age of big data. However, it needs to solve both response latency and privacy leakage. To reduce latency between users and servers while ensuring data privacy, we propose a privacy-preserving feature extraction scheme, called LightPyFE, for EEG signals in the edge computing environment. In this scheme, we design an outsourced computing toolkit, which allows the users to achieve a series of secure integer and floating-point computing operations. During the implementation, LightPyFE can ensure that the users just perform the encryption and decryption operations, where all computing tasks are outsourced to edge servers for specific processing. Theoretical analysis and experimental results have demonstrated that our scheme can successfully achieve privacy-preserving feature extraction for EEG signals, and is practical yet effective.
Keyword ：
Additive secret sharing Additive secret sharing edge computing edge computing electroencephalogram (EEG) signal electroencephalogram (EEG) signal Internet of Things (IoT) Internet of Things (IoT) privacy-preserving privacy-preserving
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| GB/T 7714 | Yan, Nazhao , Cheng, Hang , Liu, Ximeng et al. Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) : 2520-2533 . |
| MLA | Yan, Nazhao et al. "Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing" . | IEEE INTERNET OF THINGS JOURNAL 11 . 2 (2024) : 2520-2533 . |
| APA | Yan, Nazhao , Cheng, Hang , Liu, Ximeng , Chen, Fei , Wang, Meiqing . Lightweight Privacy-Preserving Feature Extraction for EEG Signals Under Edge Computing . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (2) , 2520-2533 . |
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