检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Gao, Yifan Wei, Lifang Li, Jun Chang, Xinyue Zhang, Yulong Chen, Riqing Yang, Changcai Wei, Yi Dong, Heng Digital Fujian Research Institute of Big Data for Agriculture and Forestry College of Computer and Information Science Fujian Agriculture and Forestry University Fuzhou350002 China Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210000 China Department of Obstetrics & Gynecology College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics Fujian Medical University Fuzhou350001 China School of Mathematics and Computer Science Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry Wuyi University Fujian354300 China

Medical image multi-object segmentation aims to accurately extract each object that is great significance for the medical image analysis. Although several methods based on deep leaning have been widely developed, it is still challenging due to the similarities of different objects, the significant variability of morphology, data heterogeneity and the unbalanced distribution of samples. To address the above issues, we propose a novel Morphological Guided Causal Constraint segmentation network (MCCSeg) for medical image multi-object segmentation. We build a hybrid CNN-Transformer encoder module to obtain local and global features. Specifically, a Causal Constraint Module (CCM) is proposed for feature decorrelation by sample reweighting, which utilizes weight allocation to train the weight of training samples. The Random Fourier Features (RFF) are used to solve nonlinear dependency problems and remove correlations between features for eliminating the impact of spurious correlations. The morphological guidance module (MG) is designed to extract the boundary as prior morphology information for enhancing feature representation, which are concatenated into the deep supervision module in decoder for further optimizing feature extraction. The experiments demonstrate that MCCSeg outperforms other state-of-the-art methods, obtaining up 3.76% and 5.41% improvements in DICE and HD95 scores on Synapse dataset, respectively. And the ablation experiments further verify the effectiveness of MCCSeg in improving generalization. The source code will be released at https://***/YfGAO/MCCSeg. © 2023, The Authors. All rights reserved.

关键词： Morphology

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Bionic Soft Fingers with Hybrid Variable Stiffness Mechanisms for Multimode Grasping

Bionic Soft Fingers with Hybrid Variable Stiffness Mechanism...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Xiangbo Wang Tianran Zhang Hongze Yu Zhenwei Wen Lide Fang Huaping Liu Fuchun Sun Lixue Tang Bin Fang College of Quality and Technical Supervision Hebei University Baoding China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China School of Mechanical Engineering & Automation Beihang University Beijing China International School Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Institute for Artificial Intelligence State Key Lab of Intelligent Technology and Systems Tsinghua University Beijing China School of Biomedical Engineering Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical University Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This paper presents a novel Bionic Soft Finger (BSF) that aims to overcome the limitations of conventional rigid manipulators in terms of adaptability and safety, as well as the challenges faced by soft hands regarding carrying capacity and stability. The BSF design uses a hybrid variable stiffness mechanism combining memory alloy actuators with particle jamming to achieve the desired bending angle and actuator stiffness. Our innovative approach utilizes a bionic finger design that incorporates a memory alloy skeleton and a water-cooled recirculation system, leading to a substantial reduction in the time required for each operation. Through the integration of particle jamming, we have enhanced the overall stiffness and performance of the manipulator, enabling load capacities of up to 3N per finger and more than twice the stiffness of a normal condition. Additionally, our design enables multimode grasping and incorporates a liquid metal strain sensor (METT) for real-time monitoring of finger bending angles. Comparative analyses demonstrate that our design exhibits superior stiffness and enables five-mode grasping in comparison to pneumatic actuators. We believe that bionic soft fingers present a promising solution for enhancing adaptability, safety, and performance in human-robot interaction applications.

关键词： Metals Grasping Bending Manipulators Stability analysis Skeleton Real-time systems

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Ultra-Sensitive Optical Fiber Spr Biosensor for Precise Prostate Cancer Diagnosis by Label-Free Detection of Mirna-21

SSRN

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SSRN 2025年

作者： Wang, Jiale Deng, Minghua Niu, Kunpeng Wang, Chuanquan Wang, Ning Li, Jinhua Xia, Binyun Ding, Liyun Yang, Huan Chen, Xiaohan Zhu, Jiayi National Engineering Research Center of Fiber Optic Sensing Technology and Networks Wuhan University of Technology Wuhan430070 China School of Computer and Information Engineering Hubei Normal University Huangshi China Hubei Optical Fundamental Research Center Wuhan China Collaborative Innovation Center for Advanced Organic Chemical Materials Co-constructed by the Province and Ministry Key Laboratory for the Green Preparation and Application of Functional Materials Ministry of Education Hubei Key Laboratory of Polymer Materials School of Materials Science and Engineering Hubei University Wuhan430062 China School of Physical Science and Technology Lanzhou University Lanzhou730000 China Department of Urology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan430030 China

Efficient and rapid identification of miRNA-21 may be conducive to the early diagnosis of prostate cancer. In this work, a label-free and highly sensitive optical fiber Surface Plasmon Resonance (SPR) biosensor was developed for detecting the miRNA-21, which was fabricated simply by coating a layer of Au film and AuNPs onto a multi-mode-single-mode-multi-mode (MSM) optical fiber, and then biofunctionalized with ssDNA enabling the quantification of miRNA-21. The working principle involves initiating the change of the effective refractive index (RI) around the optical fiber's surface, which was caused by nucleic acid hybridization between the ssDNA and the miRNA-21. As a result, the interaction would result in a noticeable shift of the SPR wavelengths in the transmission spectra. In the experiment, a substantial enhancement was exhibited in the RI sensitivity of the optical fiber sensor due to the surface plasmon resonance (SPR) coupling effect of AuNPs and the Au film, measuring approximately 3021.48 nm/RIU. Excitingly, the biosensors, which have a simple composition, were experimentally verified to have an ultra-low limit of detection (LOD) of 10-20 M, significantly lower than current detection methods. And trace miRNA-21 was enabled for detection without RNA amplification. The SPR sensors were also experimentally proved with excellent linearity, specificity and stability. When employed to detect the serum samples, the results indicated that the proposed sensors could brilliantly distinguish clinical patients with and without prostate cancer. Compared to the commonly used PSA quantification in clinical practice, the method developed in this work has higher detection accuracy. The proposed sensing platform processes the merits of simple configuration as well as low cost and may be applied for the precise diagnosis of prostate cancer. © 2025, The Authors. All rights reserved.

关键词： Surface plasmon resonance

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Meta: Data Compression and Event Detection Grand Challenge 2024 With SPRSound Dataset

IEEE Data Descriptions

引用

IEEE Data Descriptions 2024年 1卷 122-130页

作者： Qing Zhang Changyan Chen Shuhua Yuan Jing Zhang Jiajun Yuan Huajie Huang Yuhang Zhang Rui Pan Xuya Jiang Jian Zhao Yongfu Li Yong Yin Liebin Zhao Guoxing Wang Yong Lian Department of Micro-Nano Electronics and MoE Key Laboratory of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligence Pediatrics Shanghai China The Pediatric AI Clinical Application and Research Center Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China Department of Respiratory Medicine Shanghai Children’s Medical Center Shanghai Jiao Tong University School of Medicine Shanghai China Child Health Advocacy Institute China Hospital Development Institute Shanghai Jiao Tong University Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Children’s Medical Center School of Medicine Shanghai Jiao Tong University Sanya China School of Design Jiangnan University Wuxi China

The development of digital stethoscopes and automatic respiratory sound algorithms is crucial for accelerating diagnosis, reducing physician workload, and lowering mortality rates from respiratory diseases. However, transmitting data from digital stethoscopes to cloud or other storage devices require substantial storage and transmission capacities, especially for wearable devices used for long-term monitoring. Besides, current automatic algorithms often predict labels for segmented sound events, lacking precision in detecting the onsets and offsets of respiratory sound events. To address these challenges, we organized a grand challenge inviting the community to develop data compression and event detection algorithms to reduce the storage and transmission burden and event segmentation workload. A new testing set was prepared to evaluate the performance of the submissions. The top teams presented their work at the 20th IEEE Biomedical Circuits and Systems Conference (BioCAS) 2024. IEEE SOCIETY/COUNCIL Circuits and Systems (CAS) Society DATA TYPE/LOCATION Sound and Text; Worldwide DATA DOI/PID 10.21227/bhw7-2044; 10.21227/nfkk-1x47

关键词： Event detection Medical diagnostic imaging Predictive models Data compression Training Stethoscope Pulmonary diseases Pediatrics Error analysis Circuits and systems

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Bloch Equation Enables Physics-informed Neural Network in Parametric Magnetic Resonance Imaging

arXiv

引用

arXiv 2023年

作者： Cai, Qingrui Zhu, Liuhong Zhou, Jianjun Qian, Chen Guo, Di Qu, Xiaobo National Integrated Circuit Industry Education Integration Innovation Platform School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361102 China Fudan University Xiamen361015 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Magnetic resonance imaging (MRI) is an important non-invasive imaging method in clinical diagnosis. Beyond the common image structures, parametric imaging can provide the intrinsic tissue property thus could be used in quantitative evaluation. The emerging deep learning approach provides fast and accurate parameter estimation but still encounters the lack of network interpretation and enough training data. Even with a large amount of training data, the mismatch between the training and target data may introduce errors. Here, we propose one way that solely relies on the target scanned data and does not need a pre-defined training database. We provide a proof-of-concept that embeds the physical rule of MRI, the Bloch equation, into the loss of physics-informed neural network (PINN). PINN enables learning the Bloch equation, estimating the T2 parameter, and generating a series of physically synthetic data. Experimental results are conducted on phantom and cardiac imaging to demonstrate its potential in quantitative MRI. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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ResfEANet: ResNet-fused External Attention Network for Tuberculosis Diagnosis using Chest X-ray Images

引用

computer Methods and Programs in Biomedicine Update 2024年 5卷

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Nnani, Ann O Deng, Fuhu Ejiyi, Thomas Ugochukwu Ejiyi, Makuachukwu Bennedith Agbesi, Victor Kwaku Bamisile, Olusola Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu China College of Environmental Science and Engineering Hohai University Nanjing China Department of Pure and Industrial Chemistry University of Nigeria Nsukka Enugu Nigeria Pharmacy Department University of Nigeria Nsukka Enugu Nigeria School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Centre Chengdu University of Technology Chengdu China

Pulmonary tuberculosis (TB), the most prevalent form of TB, remains a major global public health concern, contributing to more than a million deaths each year. The accurate and timely diagnosis of this disease is of paramount importance for effective control and treatment. Chest X-ray (CXR) images have emerged as a valuable tool for screening lung diseases, including TB, owing to their cost-effectiveness and non-invasiveness. Despite advancements in technology, the challenges associated with interpreting CXR images persist, primarily due to the scarcity of trained radiologists. This underscores the pressing need for an automated and cost-effective computer-aided system capable of diagnosing TB, assisting medical practitioners in distinguishing between TB-positive and negative CXR scans. In response to this need, we introduce an innovative approach called ResNet-fused External Attention Network (ResfEANet). This network excels in accurately classifying TB from CXR images, achieving remarkable levels of accuracy and sensitivity. ResfEANet is built upon ResNet and incorporates an External Attention mechanism, albeit with fewer residual network blocks than ResNet-50 resulting in a relatively shallow network with fewer layers. This approach proves highly effective in feature extraction and yields competitive results in the classification of TB. Our method was employed to train a model that demonstrated an impressive accuracy rate of 97.59% and a remarkable sensitivity of 100% in binary classification tasks with optimal computational cost. These outcomes suggest that our proposed approach has the potential to serve as a valuable secondary tool in clinical decision-making, providing crucial assistance to radiologists and healthcare professionals. © 2023

关键词： Chest X-ray images External Attention ResfEANet ResNet Tuberculosis

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Long Text Classification Based on BERT 5

Long Text Classification Based on BERT

引用

5th IEEE Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging

arXiv

引用

arXiv 2022年

作者： Jia, Haozhe Bai, Chao Cai, Weidong Huang, Heng Xia, Yong Research & Development Institute Northwestern Polytechnical University in Shenzhen Shenzhen518057 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi'An710072 China School of Computer Science University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA15261 United States JD Finance America Corporation California CA94043 United States

In our previous work, i.e., HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated our HNF-Netv2 on the multi-modal Brain Tumor Segmentation Challenge (BraTS) 2021 dataset. The result on the test set shows that our HNF-Netv2 achieved the average Dice scores of 0.878514, 0.872985, and 0.924919, as well as the Hausdorff distances (95%) of 8.9184, 16.2530, and 4.4895 for the enhancing tumor, tumor core, and whole tumor, respectively. Our method won the RSNA 2021 Brain Tumor AI Challenge Prize (Segmentation Task), which ranks 8th out of all 1250 submitted results. Copyright © 2022, The Authors. All rights reserved.

关键词： Tumors

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Unsupervised Domain Adaptation on Person Re-Identification via Dual-level Asymmetric Mutual Learning

arXiv

引用

arXiv 2023年

作者： Wu, Qiong Li, Jiahan Dai, Pingyang Ye, Qixiang Cao, Liujuan Wu, Yongjian Ji, Rongrong Institute of Artificial Intelligence The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221000 China The Media Analytics and Computing Laboratory Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China The Peng Cheng Laboratory Shenzhen518066 China The School of Electronics Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China The Media Analytics and Computing Lab Department of Computer Science School of Informatics Xiamen University Xiamen361005 China The Youtu Laboratory Tencent Shanghai200233 China The Fujian Engineering Research Center of Trusted Artificial Intelligence Analysis and Application Institute of Artificial Intelligence Xiamen University Xiamen361005 China

Unsupervised domain adaptation person re-identification (Re-ID) aims to identify pedestrian images within an unlabeled target domain with an auxiliary labeled source-domain dataset. Many existing works attempt to recover reliable identity information by considering multiple homogeneous networks. And take these generated labels to train the model in the target domain. However, these homogeneous networks identify people in approximate subspaces and equally exchange their knowledge with others or their mean net to improve their ability, inevitably limiting the scope of available knowledge and putting them into the same mistake. This paper proposes a Dual-level Asymmetric Mutual Learning method (DAML) to learn discriminative representations from a broader knowledge scope with diverse embedding spaces. Specifically, two heterogeneous networks mutually learn knowledge from asymmetric subspaces through the pseudo label generation in a hard distillation manner. The knowledge transfer between two networks is based on an asymmetric mutual learning manner. The teacher network learns to identify both the target and source domain while adapting to the target domain distribution based on the knowledge of the student. Meanwhile, the student network is trained on the target dataset and employs the ground-truth label through the knowledge of the teacher. Extensive experiments in Market-1501, CUHK-SYSU, and MSMT17 public datasets verified the superiority of DAML over state-of-the-arts. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial applications

引用

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 131-141页

作者： Mingsheng Shang Xin Luo Zhigang Liu Jia Chen Ye Yuan MengChu Zhou the Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of Sciences the School of Computer Science and Engineering Beihang University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix *** results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

关键词： Big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning

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