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IEEE Transactions on Pattern Analysis and machine intelligence 2025年 PP卷 PP页

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Guangdong University of Technology School of Information Engineering Guangzhou China South China Technology Commercialization Center Guangzhou China

Due to the wide existence of unlabeled graph-structured data (e.g. molecular structures), the graph-level clustering has recently attracted increasing attention, whose goal is to divide the input graphs into several disjoint groups. However, the existing methods habitually focus on learning the graphs embeddings with different graph reguralizations, and seldom refer to the obvious differences in data distributions of distinct graph-level datasets. How to characteristically consider multiple graph-level datasets in a general well-designed model without prior knowledge is still challenging. In view of this, we propose a novel Graph Prompt Clustering (GPC) method. Within this model, there are two main modules, i.e., graph model pretraining as well as prompt and finetuning. In the graph model pretraining module, the graph model is pretrained by a selected source graph-level dataset with mutual information maximization and self-supervised clustering regularization. In the prompt and finetuning module, the network parameters of the pretrained graph model are frozen, and a groups of learnable prompt vectors assigned to each graph-level representation are trained for adapting different target graph-level datasets with various data distributions. Experimental results across six benchmark datasets demonstrate the impressive generalization capability and effectiveness of GPC compared with the state-of-the-art methods. © 1979-2012 IEEE.

关键词： Graph embeddings

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UD-TN: A comprehensive ultrasound dataset for benign and malignant thyroid nodule classification

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Intelligent Oncology 2025年第2期1卷 176-187页

作者： Jialin Zhu Xuzhou Fu Zhiqiang Liu Luchen Chang Xuewei Li Jie Gao Ruiguo Yu Xi Wei Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute & Hospital National Clinical Research Center for Cancer Key Laboratory of Cancer Prevention and Therapy Tianjin’s Clinical Research Center for Cancer Tianjin 300060 China College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin Key Laboratory of Advanced Networking Tianjin University Tianjin 300350 China

The automatic classification of thyroid nodules in ultrasound images is a critical research focus in medical imaging. However, publicly available thyroid ultrasound datasets remain scarce. In this study, we developed the Ultrasound Dataset for Thyroid Nodules (UD-TN), a comprehensive dataset containing 10,495 labeled images classified as benign or malignant based on pathology-confirmed results. To establish a benchmark, we proposed the Thyroid Ultrasound Image Neural Network (ThyUNet), a deep learning model designed for accurate nodule classification. By incorporating high-resolution feature enhancement, instance normalization, and dilated convolutions into residual blocks, ThyUNet excels in extracting fine-grained features, particularly for small nodules. Experimental results demonstrate that ThyUNet achieves state-of-the-art performance, with an accuracy of 89.7%, a sensitivity of 0.879, and a specificity of 0.910 on the testing set. These results surpass those of other advanced architectures, highlighting the model’s effectiveness. UD-TN and ThyUNet contribute significantly to advancing intelligent medical diagnostics. Dataset details and access instructions are available at https://***/18811755633/Sample-of-UD-TN .

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Homophily Induced Contrastive Attributed Graph Clustering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China South China Technology Commercialization Center Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China

Attributed graph clustering, aiming to discover the underlying graph structure and partition the graph nodes into several disjoint categories, is a basic task in graph data analysis. Although recent efforts over graph contrastive clustering have achieved decent performances, most of them get accustomed to construct the positive neighbor set by the generated pseudo clustering information, directly ignoring the ready-made neighbor nodes and the underlying semantics of edges in a graph. How to well deal with the graph neighbor-specific information to facilitate the performance of graph contrastive clustering is still a challenging problem. Therefore, in this work, we propose a novel Homophily Induced Contrastive Attributed Graph Clustering (HomoCAGC) method, where the power of homophily is exploited in facilitating the performance of contrastive attributed graph clustering, while the pseudo homophily in a graph is also explored and distinguished. Especially, the node feature as reliable information guidance is used to compute the underlying feature-oriented pair-wise node similarity, based on which the positive node pairs in contrastive regularizer are adjusted for better node representation characterization. According to the refined node representations, a triplet self-supervised clustering objective is well-designed to ensure the output embedding is cluster-oriented, and suitable for the downstream clustering task. Extensive experiments on seven benchmarks are conducted to demonstrate the effectiveness of HomoCAGC. © 1991-2012 IEEE.

关键词： Attributed graph clustering contrastive learning graph homophily self-supervised clustering

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Ultrasound image segmentation of thyroid nodule via latent semantic feature co-registration

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Biomedical Signal Processing and Control 2025年 108卷

作者： Li, Xuewei Zhu, Yaqiao Fan, Yilong Wei, Xi Zhang, Ruixuan Tian, Yuan Liu, Zhiqiang College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of Future Technology Tianjin University Tianjin China Department of Diagnostic and Therapeutic Ultrasonography Tianjin Medical University Cancer Institute and Hospital Tianjin China

Segmentation of nodules in thyroid ultrasound imaging plays a crucial role in the detection and treatment of thyroid cancer. However, owing to the diversity of scanner vendors and imaging protocols in different hospitals, the automatic segmentation model, which has already demonstrated expert-level accuracy in the field of ultrasound imaging segmentation, finds its accuracy reduced as the result of its weak generalization performance when being applied in clinically realistic environments. To address this issue, the present paper proposes an Atlas-based Spatial Transformer Network (ASTN), a novel framework for accurate and generalizable segmentation of thyroid nodules. The ASTN framework extracts the potential semantic features of the nodule region in ultrasound images, and realizes the co-registration and segmentation based on the topological structure, effectively improving the generalization ability and accuracy of the model. In particular, an encoder extracts and merges lesion features from the target and atlas (a template set composed of multiple real anatomical structures) for co-registration, then adaptively weights the fused labels based on stage-wise similarity to enhance model robustness and accuracy across data domains. Additionally, this paper also provides an atlas selection algorithm to retain more priori information and alleviate the challenges associated with co-registration. Thanks to the method we proposed, as shown by the evaluation results collected from the datasets of different devices, the model generalization has been greatly improved while maintaining a high level of segmentation accuracy. © 2025 Elsevier Ltd

关键词： Semantic Segmentation

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GAEM: Graph-driven Attention-based Entropy Model for LiDAR Point Cloud Compression

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Cui, Mingyue Zhong, Yuyang Feng, Mingjian Long, Junhua Ling, Yehua Xu, Jiahao Huang, Kai Sun Yat-sen University School of Computer Science and Engineering Guangzhou400100 China Sun Yat-sen University key Laboratory of Machine Intelligence and Advanced Computing Guangzhou400100 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Guangxi Transportation Science and Technology Group Co. Ltd Nanning530029 China

High-quality LiDAR point cloud (LPC) coding is essential for efficiently transmitting and storing the vast amounts of data required for accurate 3D environmental representation. The Octree-based entropy coding framework has emerged as the predominant method, however, previous study usually overly relies on large-scale attention-based context prediction to encode Octree nodes, overlooking the inherent correlational properties of this structure. In this paper, we propose a novel Graph-driven Attention-based Entropy Model (GAEM), which adopts partitioned graph attention mechanisms to uncover contextual dependencies among neighboring nodes. Different from the Cartesian coordinate-based coding mode with higher redundancy, GAEM uses the multi-level spherical Octree to organize point clouds, improving the quality of LPC reconstruction. GAEM combines graph convolution for node feature embedding and grouped-graph attention for exploiting dependency among contexts, which preserves performance in low-computation using localized nodes. Besides, to further increase the receptive field, we design a high-resolution cross-attention module introducing sibling nodes. Experimental results show that our method achieves state-of-the-art performance on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford, compared to all baselines. Compared to the benchmark GPCC, our method achieves gains of up to 53.9% and 53.6% on SemanticKITTI and Ford while compared to the sibling-introduced methods, we achieve up to 42.3% and 44.7% savings in encoding/decoding time. In particular, our GAEM allows for extension to downstream tasks (i.e., vehicle detection and semantic segmentation), further demonstrating the practicality of the method. (Figure presented) © 1991-2012 IEEE.

关键词： Semantic Segmentation

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A Chinese Expressive Long-dialogue Speech Dataset with Scripts

A Chinese Expressive Long-dialogue Speech Dataset with Scrip...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jin Li Tianrui Wang Meng Ge Chenrui Cui Jiahui Zhao Jianrong Wang Longbiao Wang Jianwu Dang Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China College of Intelligence and Computing Tianjin University Tianjin China Huiyan Technology (Tianjin) Co. Ltd Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the advancement of large-scale models, the demand for emotionally rich, long-context, and highly natural communication in human-computer interaction increases. However, the exploration of long-context or script-level speech conversation tasks remains limited due to the lack of specific supervised data. To address this, we introduce a three-stage data processing pipeline for creating a Chinese expressive long-dialogue speech dataset with scripts (CELSDS). We collect videos from TV series, manually annotate speaker information for each character, apply Optical Character Recognition (OCR) to extract speech content, annotate episode summaries, and use a large language model (LLM) to generate sentence-level scenario descriptions. To our knowledge, this is the first Chinese long-context dialogue dataset that incorporates speaker and content annotations, script-level episode summaries, and sentence-level scenario details. Using this dataset, we develop a baseline model for both speech-to-script and script-to-speech generation tasks. The annotations and data production code are open-sourced at: https://***/lijin0120/CELSDS.

关键词： TV Annotations Pipelines Optical character recognition Speech recognition Production Signal processing Data processing Speech processing Videos

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Boosting Lightweight Camouflaged Object Detection with Multi-Scale Context and Boundary Awareness

Boosting Lightweight Camouflaged Object Detection with Multi...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zihan Xu Zheng Wang Haoyu Wang Cheng Liu Yan Zhou Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Machine Learning Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

To adapt to the resource-limited environment, this study introduces the lightweight boundary-aware camouflaged object detection(COD) network LMABnet. We enhance the feature representation capability of the lightweight network through a multi-scale feature fusion architecture, while effectively avoids the model inflation and parameter redundancy of traditional COD methods. The LMABnet adopts the streamlined two-branch architecture that incorporates with the lightweight feature extraction module, which provides the multi-scale feature perception capability and reduces the loss of spatial information caused by rapid downsampling. In addition, based on the principle of "easy before difficult", the lightweight multi-scale feature fusion module discovers the key parts of the camouflaged object through the details. Then enhances the recognition of the object edges by using the edge-attention fusion module, so as to improve the accuracy performance of detection. Empirical evaluations conducted on the COD dataset demonstrate that the proposed methodology not only attains state-of-the-art performance but also accomplishes a significant reduction of 40% in parameter count relative to existing approaches.

关键词： Accuracy Image edge detection Scalability Redundancy Computer architecture Object detection Signal processing Feature extraction Real-time systems Speech processing

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Multi-scale Re-weighted Attention Feature Fusion for Non-Intrusive Load Monitoring

Multi-scale Re-weighted Attention Feature Fusion for Non-Int...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lingxi Yang Meijun Sun Haowei Ran Yipu Liu Yan Zhou Zheng Wang College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Machine Learning Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Non-Intrusive Load Monitoring (NILM) addresses the challenge of disaggregating total energy consumption into individual appliance usage, which is essential for enhancing energy efficiency and managing smart grids. Existing methods often overlook the impact of window sizes on the separation of appliance-level power signals, leading to appliance power aliasing (APA), where a single total power curve corresponds to multiple operating conditions and appliance couplings. A larger window captures global features noise, but it also exacerbates APA, thereby enlarging the solution space. In contrast, a smaller window mitigates APA but it introduces noise and increases complexity. To address these limitations, we propose the Multi-Scale Re-weighted Attention Feature Fusion (MRAFF) model, which employs a multi-scale framework to manage temporal variations and incorporates a re-weighted attention module to enhance multi-scale interactions, thereby improving feature representation. Experimental results demonstrate that our MRAFF model significantly outperforms baseline models on the REDD and UK-DALE datasets, achieving improvements ranging from 2.27% to 10.5% in MAE and from 33.74% to 58.34% in SAE.

关键词： Couplings Load monitoring Energy consumption Noise Energy efficiency Distance measurement Smart grids Complexity theory Speech processing Load modeling

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Joint Edge and Regional Depth Enhancement Network for Camouflaged Object Detection

Joint Edge and Regional Depth Enhancement Network for Camouf...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Miao Qi Zheng Wang Cheng Liu Yan Zhou Meijun Sun College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Machine Learning Tianjin University Tianjin China School of Disaster and Emergency Medicine Tianjin University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Camouflaged object detection (COD) is a task of identifying and locating target objects that are camouflaged, masked, or confused. Research claims that depth cues can provide effective object location cues. However, depth images often contain noise interference, which may negatively affect object recognition. In addition, depth images also lack edge details, and most of previous works pay more attention to the integrity of the region, rather than the quality of the edge. To solve these two problems, we propose a joint edge and regional depth enhancement network (ERDENet) for Camouflaged Object Detection. The network first introduces the Locate and Generate Depth (LGD) module to locate the target region and generate its depth image. After that, the Feature Interactive Fusion (FIF) module is carried out, complementing the enhanced depth feature with the feature extracted from the original image, and then fuse the edge clues into it. Finally, we design a Muiti-modal Refinement Extraction (MRE) module to refine the feature to improve the detection performance. Extensive experiments show that our method has advantages and effectiveness.

关键词： Fuses Image edge detection Noise Object detection Interference Feature extraction Acoustics Object recognition Speech processing

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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

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Optics Express 2025年第11期33卷 22208-22221页

作者： Chen-Rui Fan Bo Lu Yi-Xuan Yao Qing Ai Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing 100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou 450001 China lubo@*** School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing 100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing 100875 China wangchuan@***

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications.

关键词： Neural networks Nonlinear optical fibers Numerical simulation Optical parametric oscillators Optical systems Quantum noise

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