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Teeth YOLACT:An Instance Segmentation Model Based on Impacted Tooth Panoramic X-Ray images

computers, Materials & Continua 2025年第6期83卷 4815-4834页

作者： Tao Zhou Yaxing Wang Huiling Lu Wenwen Chai Yunfeng Pan Zhe Zhang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China

The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly ***,due to the complex structure,low contrast,and complex background of teeth in panoramic X-ray images,the task of instance segmentation is technically *** this study,the contrast between impacted Teeth and periodontal tissues such as gingiva,periodontalmembrane,and alveolar bone is low,resulting in fuzzy boundaries of impacted *** based on Teeth YOLACT is proposed to provide amore efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray ***,a Multi-scale Res-Transformer Module(MRTM)is *** the module,depthwise separable convolutions with different receptive fields are used to enhance the sensitivity of the model to lesion ***,the Vision Transformer is integrated to improve the model’s ability to perceive global ***,the Context Interaction-awareness Module(CIaM)is designed to fuse deep and shallow *** deep semantic features guide the shallow spatial ***,the shallow spatial features are embedded into the deep semantic features,and the cross-weighted attention mechanism is used to aggregate the deep and shallow features efficiently,and richer context information is ***,the Edge-preserving perceptionModule(E2PM)is designed to enhance the teeth edge *** first-order differential operator is used to get the tooth edge weight,and the perception ability of tooth edge features is *** shallow spatial feature is fused by linear mapping,weight concatenation,and matrix multiplication operations to preserve the tooth edge ***,comparison experiments and ablation experiments are conducted on the oral panoramic X-ray image *** results show that the APdet,APseg,ARdet,ARseg,mAPdet,and mAPseg indicators of the proposed model are 89.9%,91.9%,77.4%,77.6%,72.8%,and 73.5%,*** study further verifies the a

关键词： The oral panoramic X-ray instance segmentation impacted teeth vision transformer the edge-preserving

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Guided-YNet: Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network

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computers, Materials & Continua 2024年第9期80卷 4813-4832页

作者： Tao Zhou Yunfeng Pan Huiling Lu Pei Dang Yujie Guo Yaxing Wang School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzuUniversityYinchuan750021China

Multimodal lung tumor medical images can provide anatomical and functional information for the same *** as Positron Emission Computed Tomography(PET),Computed Tomography(CT),and *** to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key *** solve the problem,the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network(Guide-YNet)is proposed in this ***,a double-encoder single-decoder U-Net is used as the backbone in this model,a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and transmit it into the skip connection of the backbone,and the high sensitivity of PET images to tumors is used to guide the network to accurately locate ***,a Cross Scale Feature Enhancement Module(CSFEM)is designed to extract multi-scale fusion features after ***,a Cross-Layer Interactive Feature Enhancement Module(CIFEM)is designed in the encoder to enhance the spatial position information and semantic ***,a Cross-Dimension Cross-Layer Feature Enhancement Module(CCFEM)is proposed in the decoder,which effectively extractsmultimodal image features through global attention and multi-dimension local *** proposed method is verified on the lung multimodal medical image datasets,and the results showthat theMean Intersection overUnion(MIoU),Accuracy(Acc),Dice Similarity Coefficient(Dice),Volumetric overlap error(Voe),Relative volume difference(Rvd)of the proposed method on lung lesion segmentation are 87.27%,93.08%,97.77%,95.92%,89.28%,and 88.68%,*** is of great significance for computer-aided diagnosis.

关键词： Medical image segmentation U-Net saliency feature guidance cross-modal feature enhancement cross-dimension feature enhancement

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Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

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computers, Materials & Continua 2024年第7期80卷 449-466页

作者： Qunyue Mu Qiancheng Yu Chengchen Zhou Lei Liu Xulong Yu The College of Computer Science and Engineering North Minzu UniversityYinchuan750021China The Key Laboratory of Images and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic *** effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric ***,manual enforcement by traffic police is time-consuming and *** methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning *** paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these *** proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional *** modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic *** results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in ***,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

关键词： YOLOv8 object detection electric bicycle helmet detection electric bicycle license plate detection

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MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

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computers, Materials & Continua 2024年第6期79卷 4371-4393页

作者： Huiling Lu Tao Zhou School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China School of Computer Science and Engineering North Minzu UniversityYinchuan750021China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission NorthMinzu UniversityYinchuan750021China

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided ***,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are *** solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung *** pixel dependence relationship is established in local and non-local fields to improve the model perception ***,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained ***,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic ***,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image *** results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this *** method has positive significance for the detection of lung tumors.

关键词： PET/CT images instance segmentation mask RCNN interactive fusion transformer

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AN ADVERSARIAL AND DEEP HASHING-BASED HIERARCHICAL SUPERVISED CROSS-MODAL image AND TEXT RETRIEVAL ALGORITHM

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International Journal of Robotics and Automation 2024年第1期39卷 77-86页

作者： Chen, Ruidong Qiang, Baohua Zhou, Mingliang Zhang, Shihao Zheng, Hong Tang, Chenghua Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Computer Science Chongqing University Chongqing400044 China

With the rapid development of robotics and sensor technology, vast amounts of valuable multimodal data are collected. It is extremely critical for a variety of robots performing automated tasks to find relevant multimodal information quickly and efficiently in large amounts of data. In this paper, we propose an adversarial and deep hashing-based hierarchical supervised cross-modal image and text retrieval algorithm to perform semantic analysis and association modelling on image and text by making full use of the rich semantic information of the label hierarchy. First, the modal adversarial block and the modal differentiation network both perform adversarial learning to keep different modalities with the same semantics closest to each other in a common subspace. Second, the intra-label layer similarity loss and inter-label layer correlation loss are used to fully exploit the intrinsic similarity existing in each label layer and the correlation existing between label layers. Finally, an objective function for different semantic data is redesigned to keep data with different semantics away from each other in a common subspace, thus avoiding interference of retrieval by data of different semantics. The experimental results on two cross-modal retrieval datasets with hierarchically supervised information show that the proposed method substantially enhances retrieval performance and consistently outperforms other state-of-the-art methods. © 2024 Acta Press. All rights reserved.

关键词： Semantics

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Hierarchical Optimization Method for Federated Learning with Feature Alignment and Decision Fusion

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computers, Materials & Continua 2024年第10期81卷 1391-1407页

作者： Ke Li Xiaofeng Wang Hu Wang College of Computer Science and Engineering North Minzu UniversityYinchuan750021China The Key Laboratory of Images&Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

In the realm of data privacy protection,federated learning aims to collaboratively train a global ***,heterogeneous data between clients presents challenges,often resulting in slow convergence and inadequate accuracy of the global *** shared feature representations alongside customized classifiers for individual clients emerges as a promising personalized ***,previous research has frequently neglected the integration of global knowledge into local representation learning and the synergy between global and local classifiers,thereby limiting model *** tackle these issues,this study proposes a hierarchical optimization method for federated learning with feature alignment and the fusion of classification decisions(FedFCD).FedFCD regularizes the relationship between global and local feature representations to achieve alignment and incorporates decision information from the global classifier,facilitating the late fusion of decision outputs from both global and local ***,FedFCD employs a hierarchical optimization strategy to flexibly optimize model *** experiments on the Fashion-MNIST,CIFAR-10 and CIFAR-100 datasets,we demonstrate the effectiveness and superiority of *** instance,on the CIFAR-100 dataset,FedFCD exhibited a significant improvement in average test accuracy by 6.83%compared to four outstanding personalized federated learning ***,extended experiments confirm the robustness of FedFCD across various hyperparameter values.

关键词： Federated learning data heterogeneity feature alignment decision fusion hierarchical optimization

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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

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computers, Materials & Continua 2024年第6期79卷 4863-4882页

作者： Tao Zhou Yujie Guo Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

computer-aided diagnosis of pneumonia based on deep learning is a research ***,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this *** main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale *** MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features ***,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature ***,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different *** verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried *** the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,*** the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,*** model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia computer-Aided Diagnosis.

关键词： Pneumonia X-ray image ResNet multi-scale feature direction feature transformer

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Spatiality-Aligned Graph Convolution Hashing for Unsupervised Cross-Modal Retrieval 2

Spatiality-Aligned Graph Convolution Hashing for Unsupervise...

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2nd IEEE International Conference on Signal, Information and Data processing, ICSIDP 2024

作者： Li, Fang Dai, Liulian Lan, Rushi Yang, Rui Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin541004 China

ISBN: (纸本)9798331515669

Unsupervised cross-modal retrieval leveraging hash learning has attracted a lot of interest from academics, primarily due to its minimal storage requirements, swift retrieval speeds, and label-free nature, making it a prime candidate for real-world applications. Nevertheless, the inherent "heterogeneous differences"and "semantic gap"within cross-modal data features pose significant challenges. Despite various attempts by scholars to address these issues, the existing solutions often fall short in terms of effectiveness. This can be attributed to several reasons: Firstly, image features, in contrast to text features, possess a plethora of attributes, making it challenging to extract features that possess strong expressive capabilities. Secondly, each modality of data exhibits not only inter-node correlations but also intricate relationships between individual nodes and their neighboring nodes. Consequently, a straightforward semantic correlation matrix is inadequate in capturing the intricate structural relationships within the data. Thirdly, the neglect of internal structural associations results in insufficient representation capabilities of binary hash codes in the Hamming space, hampering the learning of effective hash functions. To address these limitations, we introduce a Spatiality-Aligned Graph Convolution Hashing (SAGCH) algorithm. Extensive experiments demonstrate that our SAGCH method excels in both "image-to-text retrieval"and "text-to-image retrieval"tasks. © 2024 IEEE.

关键词： Semantics

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A text dependent copy-paste plagiarism and text-rewriting plagiarism model

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Journal of intelligent and Fuzzy Systems 2025年第6期48卷 849-858页

作者： Guo, Qingkai Huang, Guimin Wang, Yabing Zhou, Ya School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin China

Plagiarism is common in English writing exams. Researchers classify plagiarism into copy-paste and text-rewriting plagiarism, but existing models need help with problems such as the single way of checking and unsatisfactory results. Aiming at the copy-paste problem in English writing plagiarism, this paper proposes a digital fingerprint model based on the N-Gram window jumping mechanism. The model incorporates a sliding window and an improved matching tool to solve the problems of excessive fingerprint density and low checking efficiency in text extraction. Meanwhile, the model adds a Fisher-Yates shuffle algorithm with a salt parameter to crack the hash collision in text matching. The experiments show that the model can detect copy-paste plagiarism in English composition. For text rewriting plagiarism, this paper designs a TextCNN-BiGRU-based model, which combines TextCNN and BiGRU so that the extracted text semantic information considers the text’s local and contextual features. The experiments show that the model improves the accuracy by 1.9 percentage points and the F1 value by 1.2 percentage points on the MRPC dataset compared with other models. © 2025 – IOS Press. All rights reserved.

关键词： copy and paste Plagiarism checking text rewriting

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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

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Frontiers of computer Science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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