检索结果-南通市图书馆

Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

computer Modeling in Engineering & sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril department of computer science and Engineering College of Engineering GuindyAnna UniversityChennai600025India department of Information Technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

引用

computer Modeling in Engineering & sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng department of computer science and Engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and computer Engineering Louisiana State UniversityBaton RougeLA 70803USA department of computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of Automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

引用

computer Modeling in Engineering & sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan department of Computational Intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Single-cell omics: experimental workflow, data analyses and applications

引用

science China Life sciences 2025年第1期68卷 5-102页

作者： Fengying Sun Haoyan Li Dongqing Sun Shaliu Fu Lei Gu Xin Shao Qinqin Wang Xin Dong Bin Duan Feiyang Xing Jun Wu Minmin Xiao Fangqing Zhao Jing-Dong J.Han Qi Liu Xiaohui Fan Chen Li Chenfei Wang Tieliu Shi department of Clinical Laboratory the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City) Pharmaceutical Informatics institute College of Pharmaceutical SciencesZhejiang University Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University) Ministry of EducationOrthopaedic DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Frontier science Center for Stem Cells School of Life Sciences and TechnologyTongji University Translational Medical Center for Stem Cell Therapy and institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Research institute of Intelligent Computing Zhejiang Lab Shanghai Research institute for Intelligent Autonomous Systems Center for Single-cell Omics School of Public HealthShanghai Jiao Tong University School of Medicine National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang University Center for bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Beijing institutes of Life science Chinese Academy of Sciences Peking-Tsinghua Center for Life sciences Academy for Advanced Interdisciplinary StudiesCenter for Quantitative Biology (CQB)Peking University Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases Women's HospitalZhejiang University School of Medicine Key Laboratory of Advanced Theory and Application in Statistics and Data science-MOE School of StatisticsEast China Normal University

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome,proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial sc RNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field,offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

关键词： single-cell sequencing genome epigenome proteomics metabolomics multimodal spatial transcriptomics CRISPR screening

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Single-cell technologies: current and near future

引用

science China Life sciences 2025年第01期 1-4页

作者： Chenfei Wang Qi Liu Xiaohui Fan Tieliu Shi Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration Ministry of EducationOrthopedics DepartmentTongji HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Frontier science Center for Stem Cells School of Life Sciences and TechnologyTongji University Translational Medical Center for Stem Cell Therapy and institute for Regenerative Medicine Shanghai East HospitalBioinformatics DepartmentSchool of Life Sciences and TechnologyTongji University Research institute of Intelligent Computing Zhejiang Lab Pharmaceutical Informatics institute College of Pharmaceutical SciencesZhejiang University National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang University Center for bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Key Laboratory of Advanced Theory and Application in Statistics and Data science (MOE) School of StatisticsEast China Normal University

Single-cell technologies enable the indepth exploration of multiple biological hierarchies at the scale of individual cells,which have deepened our knowledge of cellular diversity, tissue organization, and overall organism function(Sun et al.,2024). In 2009, single-cell RNA sequencing(sc RNA-seq) was first developed as a powerful tool to dissect gene expression and uncover transcriptional *** the rapid advancement of technology, single-cell approaches have expanded to encompass other omics, such as genomics, epigenomics, proteomics,and metabolomics.

关键词：

来源：

同方期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Small extrachromosomal circular DNA in amyotrophic lateral sclerosis matter

引用

Neural Regeneration Research 2025年第5期20卷 1411-1413页

作者： Marcos J.Arauzo-Bravo Daniela Gerovska Matthias Schwab Alexandra Kretz Computational Biology and Systems Biomedicine Biodonostia Health Research InstituteSan SebastianSpain Basque Foundation for science IKERBASQUEBilbaoSpainMax Planck Institute for Molecular BiomedicineComputational Biology and Bioinformatics GroupMünsterNorth Rhine-WestphaliaGermany department of Cell Biology and Histology Faculty of Medicine and NursingUniversity of Basque Country(UPV/EHU)LeioaSpain department of Neurology Jena University HospitalJenaThuringiaGermany

Comprehensive studies identify motor neuron spectrum disorders including amyotrophic lateral sclerosis(ALS)as globally rising fatal disorders with the highest prevalence in aging populations,influenced by ethnicity and ancestry(GBD 2016 Motor Neuron Disease Colla borators,2018).While~10% of diagnoses involve a family history(fALS),most cases are considered sporadic(sALS).However,population-based studies suggest that even cases without a common index mutation impart heritability(Ryan et al.,2019),indicating a crucial role of rare and as yet unknown genetic denominators.

关键词： amyotrophic sclerosis

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A Novel Structural Measure Separating Non-Coding RNAs from Genomic Backgrounds

引用

Tsinghua science and Technology 2015年第5期20卷 474-483页

作者： Yingfeng Wang Russell L.Malmberg Liming Cai the School of Information Technology Middle Georgia State University department of Plant Biology and institute of bioinformatics University of Georgia department of computer science and institute of bioinformatics University of Georgia

RNA secondary structure has become the most exploitable feature for ab initio detection of non-coding RNA（nc RNA） genes from genome sequences. Previous work has used Minimum Free Energy（MFE） based methods developed to identify nc RNAs by measuring sequence fold stability and certainty. However, these methods yielded variable performances across different nc RNA species. Designing novel reliable structural measures will help to develop effective nc RNA gene finding tools. This paper introduces a new RNA structural measure based on a novel RNA secondary structure ensemble constrained by characteristics of native RNA tertiary structures. The new method makes it possible to achieve a performance leap from the previous structure-based methods. Test results on standard nc RNA datasets（benchmarks） demonstrate that this method can effectively separate most nc RNAs families from genome backgrounds.

关键词： RNA secondary structure RNA tertiary structure Inside algorithm

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

引用

computer Modeling in Engineering & sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India computer Engineering Ronin InstituteMontclairNJ 07043USA department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&department of computer science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials

引用

computer Modeling in Engineering & sciences 2025年第1期142卷 713-732页

作者： Petr Opela Josef Walek Jaromír Kopecek department of Metallurgical Technologies Faculty of Materials Science and TechnologyVSB–Technical University of OstravaOstrava70800Czech Republic FZU—institute of Physics of the Czech Academy of sciences Prague18200Czech Republic

In engineering practice,it is often necessary to determine functional relationships between dependent and independent *** relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable ***,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely *** study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic *** ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon *** the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis.

关键词： Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Three-Stage Transfer Learning with AlexNet50 for MRI Image Multi-Class Classification with Optimal Learning Rate

引用

computer Modeling in Engineering & sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of Computing Sastra Deemed to be UniversityThanjavur613401India department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathur603203India department of AI and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI *** manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRI tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：