检索结果-内蒙古大学图书馆

Application of Graph-Curvature Features in Auxiliary Diagnosis for Histopathological Image Identification of Gastric Cancer

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： He, Ruilin Li, Chen Yang, Xinyi Yang, Jinzhu Jiang, Tao Grzegorzek, Marcin Sun, Hongzan Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang China School of Intelligent Medicine Chengdu University of Traditional Chinese Medicine Chengdu China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information Technology Chengdu China Institute for Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Shengjing Hospital of China Medical University Shenyang China School of Computer Science and Engineering

Background: Histopathology diagnosis is often regarded as the final diagnostic method for malignant tumors, but it has some drawbacks. This paper explores a computer-aided diagnostic method that can identify benign and malignant gastric cancer with histopathology images. Method: This article obtains the most suitable process through multiple experiments, compared multiple methods and features for classification. Firstly, the U-net is applied to segment the image. Next, the nucleus is extracted from the segmented image and the Minimum Spanning Tree (MST) diagram structure is drawn. The third step is to extract the graph-curvature features of histopathology image according to the MST image. Finally, by inputting graph-curvature features into the classifier, the recognition results for benign or malignant can be obtained. Result: During the experiment, we use various methods for comparison. In the image segmentation stage, U-net, watershed algorithm and Otsu threshold segmentation methods are used respectively. Combined with multiple indicators, we find that the U-net method is the most suitable for segmentation of histopathology images. In the feature extraction stage, in addition to extracting graph-edge feature and graph-curvature feature, several basic image features are also extracted, including Red, Green, Blue feature, Gray-Level Co-occurrence Matrix feature, Histogram of Oriented Gradient feature, and Local Binary Pattern feature. In the classifier design stage, we experimented with various methods, such as Support vector machine (SVM), Random forest, Artificial Neural Network, K Nearest Neighbors, VGG-16 and Inception-V3. Through the comparison and analysis, the classification results with an accuracy of 98.57% can be obtained by inputting the graph-curvature feature into SVM classifier. Conclusion: This paper has created a unique feature, graph-curvature feature based on MST to represent and analyze histopathology images. This graph-based feature can be used

关键词： Local binary pattern

Dadm: Enhancing Eeg Artifact Removal by Fusing Artifact Representation into the Time-Frequency Domain

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Li, Haoran Feng, Fan Kang, Jiarong Zhang, Jin Gong, Xiaoli Lu, Tingjuan Sun, Zhe Solé-Casals, Jordi College of Computer Science Nankai University Tianjin China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China State Key Laboratory of High-end Server and Storage Technology China 903rd Hospital of PLA China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Data and Signal Processing Group University of Vic Central University of Catalonia Catalonia Vic08500 Spain

The electroencephalogram (EEG) provides essential data for analyzing brain activities. However, artifacts such as electrooculography (EOG) and electromyography (EMG) often interleave with the EEG signals, significantly affecting the quality of EEG signal analysis. The heterogeneous distribution of these artifacts in the time-frequency domain makes it challenging to remove multiple artifacts using a unified model. In this paper, we propose an Artifact-Aware EEG Denoising Model, referred to as DADM, to effectively remove various types of artifacts in a unified manner. We first obtain an artifact representation that indicates the type of artifact from a pre-trained artifact classification model. This artifact representation is then used as prior knowledge, which is fused into the denoising model in the frequency-time domain. This enables the model to become aware of the artifact type and precisely remove artifacts based on their type. Due to the heterogeneous distributions of artifacts in the frequency domain, we introduce a frequency enhancement module that can identify specific types of artifacts based on their representation and remove them using a hard attention mechanism. Additionally, we design a time-domain compensation module to enhance the denoising capability of DADM by compensating for potential losses of global information. Comprehensive experiments demonstrate that DADM significantly outperforms the Novel CNN in denoising EEG signals, showing a notable 7.77% improvement in correlation coefficient (CC) metrics. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

Reconfigurable intelligent Surface With Energy Harvesting Assisted Cooperative Ambient Backscatter Communications

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Ma, Hui Zhang, Haijun Zhang, Ning Wang, Jianquan Wang, Ning Leung, Victor C.M. University of Science and Technology Beijing Beijing100083 China The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis School of Information Engineering Zhengzhou University Zhengzhou450000 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

The performance of cooperative ambient backscatter communications (CABC) can be enhanced by employing reconfigurable intelligent surface (RIS) to assist backscatter transmitters. Since the RIS power consumption is a non-negligible issue, we consider a RIS assisted CABC system where the RIS with energy harvesting circuit can not only reflect signal but also harvest wireless energy. We study a transmission design problem to minimize the RIS power consumption with the quality of service constraints for both active and backscatter transmissions. The optimization problem is a mixed-integer non-convex programming problem which is NP-hard. To tackle it, an algorithm is proposed by employing the block coordinate descent, semidefinite relaxation and alternating direction method of multipliers techniques. Simulation results demonstrate the effectiveness of the proposed algorithm. © 2022, CC BY-NC-ND.

关键词： Convex optimization

Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhou, Yirong Wu, Yanhuang Su, Yuhan Li, Jing Cai, Jianyun You, Yongfu Guo, Di Qu, Xiaobo The Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen361104 China The Department of Electronic Science Key Laboratory of Digital Fujian on IoT Communication Architecture and Safety Technology Xiamen University Xiamen361104 China Shanghai Electric Group CO. LTD Shanghai200001 China China Telecom Group Quanzhou362000 China China Mobile Group Xiamen361005 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) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing, 6G bandwidth, edge computing, federated learning, and blockchain technology. This system is called Cloud-MRI, aiming at solving the problems of MRI data storage security, transmission speed, AI algorithm maintenance, hardware upgrading, and collaborative work. The workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data (ISMRMRD) format. Then, the data are uploaded to the cloud or edge nodes for fast image reconstruction, neural network training, and automatic analysis. Then, the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other services. The Cloud-MRI system will save the raw imaging data, reduce the risk of data loss, facilitate inter-institutional medical collaboration, and finally improve diagnostic accuracy and work efficiency. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

A Dynamic Transformer Network for Vehicle Detection

学校读者我要写书评

暂无评论

IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Chunwei Liu, Kai Zhang, Bob Huang, Zhixiang Lin, Chia-Wen Zhang, David Harbin Institute of Technology School of Computer Science and Technology Harbin15001 China Macao Special Administrative Region of China PAMI Research Group University of Macau 999078 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Key Laboratory of Electromagnetic Environmental Sensing Hefei230601 China National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu Taiwan School of Data Science Shenzhen518172 China

Stable consumer electronic systems can assist traffic better. Good traffic consumer electronic systems require collaborative work between traffic algorithms and hardware. However, performance of popular traffic algorithms containing vehicle detection methods based on deep networks via learning data relation rather than learning differences in different lighting and occlusions is limited. In this paper, we present a dynamic Transformer network for vehicle detection (DTNet). DTNet utilizes a dynamic convolution to guide a deep network to dynamically generate weights to enhance adaptability of an obtained detector. Taking into relations of different information account, a mixed attention mechanism based channel attention and Transformer is exploited to strengthen relations of channels and pixels to extract more salient information for vehicle detection. To overcome the drawback of difference in an image account, a translation-variant convolution relies on spatial location information to refine obtained structural information for vehicle detection. Experimental results illustrate that our DTNet is competitive for vehicle detection. Code of the proposed DTNet can be obtained at https://***/hellloxiaotian/DTNet. © 2025 IEEE.

关键词： Dynamic convolution Hybrid Attention Mechanism Transformer Vehicle Detection

Value Function Dynamic Estimation in Reinforcement Learning based on data Adequacy 20

学校读者我要写书评

暂无评论

Value Function Dynamic Estimation in Reinforcement Learning ...

4th High Performance Computing and Cluster Technologies Conference, HPCCT 2020 and the 3rd International Conference on Big data and Artificial Intelligence, BDAI 2020

作者： Gao, Huifan Pan, Yinghui Tang, Jing Zeng, Yifeng Chai, Peihua Cao, Langcai Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University China Newcastle Business School Northumbria University United Kingdom Department of Computer and Information Sciences Northumbria University United Kingdom

ISBN: (纸本)9781450375603

In recent years, reinforcement learning has played an important role in the study of decision problem in computer games. To solve the problem of how to better estimate the value function with limited computational resources, this paper proposes a dynamic estimation method of value function based on data adequacy. In consideration of the varying complexity of each state in the MDP model, we propose a dynamic value function estimation method which is different from the fixed value function estimation method in traditional methods. Based on the PigChase challenge of the Malmo project launched by Microsoft in 2017, we compare the new method with the existing techniques. Experimental results show that the performance of the proposed algorithm is better than traditional algorithms. © 2020 ACM.

关键词： Dynamic programming

Towards Expressive Spectral-Temporal Graph Neural Networks for Time Series Forecasting

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jin, Ming Shi, Guangsi Li, Yuan-Fang Xiong, Bo Zhou, Tian Salim, Flora D. Zhao, Liang Wu, Lingfei Wen, Qingsong Pan, Shirui School of Information and Communication Technology Griffith University Gold Coast Australia Department of Data Science and AI Monash University Melbourne Australia International Max Plank Research School for Intelligent Systems The University of Stuttgart Stuttgart Germany Alibaba Group Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney Australia Department of Computer Science Emory University Atlanta United States Anytime.AI New York United States Squirrel Ai Learning Bellevue United States

Time series forecasting has remained a focal point due to its vital applications in sectors such as energy management and transportation planning. Spectral-temporal graph neural network is a promising abstraction underlying most time series forecasting models that are based on graph neural networks (GNNs). However, more is needed to know about the underpinnings of this branch of methods. In this paper, we establish a theoretical framework that unravels the expressive power of spectral-temporal GNNs. Our results show that linear spectral-temporal GNNs are universal under mild assumptions, and their expressive power is bounded by our extended first-order Weisfeiler-Leman algorithm on discrete-time dynamic graphs. To make our findings useful in practice on valid instantiations, we discuss related constraints in detail and outline a theoretical blueprint for designing spatial and temporal modules in spectral domains. Building on these insights and to demonstrate how powerful spectral-temporal GNNs are based on our framework, we propose a simple instantiation named Temporal Graph Gegenbauer Convolution (TGGC), which significantly outperforms most existing models with only linear components and shows better model efficiency. Our findings pave the way for devising a broader array of provably expressive GNN-based models for time series. © 2023, CC BY.

关键词： Time series

Joint Optimization of Resource Allocation and User Association in Multi-Frequency Cellular Networks Assisted by RIS

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Qiao, Yuanyuan Niu, Yong Han, Zhu Mao, Shiwen He, Ruisi Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The School of Information Engineering Zhengzhou University Zhengzhou450001 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Due to the development of communication technology and the rise of user network demand, a reasonable resource allocation for wireless networks is the key to guaranteeing regular operation and improving system performance. Various frequency bands exist in the natural network environment, and heterogeneous cellular network (HCN) has become a hot topic for current research. Meanwhile, Reconfigurable intelligent Surface (RIS) has become a key technology for developing next-generation wireless networks. By modifying the phase of the incident signal arriving at the RIS surface, RIS can improve the signal quality at the receiver and reduce co-channel interference. In this paper, we develop a RIS-assisted HCN model for a multi-base station (BS) multi-frequency network, which includes 4G, 5G, millimeter wave (mmwave), and terahertz networks, and considers the case of multiple network coverage users, which is more in line with the realistic network characteristics and the concept of 6G networks. We propose the optimization objective of maximizing the system sum rate, which is decomposed into two subproblems, i.e., the user resource allocation and the phase shift optimization problem of RIS components. Due to the NP-hard and coupling relationship, we use the block coordinate descent (BCD) method to alternately optimize the local solutions of the coalition game and the local discrete phase search algorithm to obtain the global solution. In contrast, most previous studies have used the coalition game algorithm to solve the resource allocation problem alone. Simulation results show that the algorithm performs better than the rest of the algorithms, effectively improves the system sum rate, and achieves performance close to the optimal solution of the traversal algorithm with low complexity. Copyright © 2023, The Authors. All rights reserved.

关键词： Resource allocation

A Comprehensive Survey for Hyperspectral Image Classification: The Evolution from Conventional to Transformers and Mamba Models

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Ahmad, Muhammad Distefano, Salvatore Khan, Adil Mehmood Mazzara, Manuel Li, Chenyu Li, Hao Aryal, Jagannath Ding, Yao Vivone, Gemine Hong, Danfeng Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Dipartimento di Matematica e Informatica-MIFT University of Messina Messina98121 Italy School of Computer Science University of Hull HullHU6 7RX United Kingdom Institute of Software Development and Engineering Innopolis University Innopolis420500 Russia Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Big Geospatial Data Management Technical University of Munich Munich85521 Germany Department of Infrastructure Engineering University of Melbourne ParkvilleVIC Australia Intelligent Control Laboratory PLA Rocket Force University of Engineering Xi’an710025 China Institute of Methodologies for Environmental Analysis National Research Council Tito85050 Italy School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China

Hyperspectral Image Classification (HSC) presents significant challenges owing to the high dimensionality and intricate nature of Hyperspectral (HS) data. While traditional Machine Learning (TML) approaches have demonstrated effectiveness, they often encounter substantial obstacles in real-world applications, including the variability of optimal feature sets, subjectivity in human-driven design, inherent biases, and methodological limitations. Specifically, TML suffers from the curse of dimensionality, difficulties in feature selection and extraction, insufficient consideration of spatial information, limited robustness against noise, scalability issues, and inadequate adaptability to complex data distributions. In recent years, Deep Learning (DL) techniques have emerged as robust solutions to address these challenges. This survey offers a comprehensive overview of current trends and future prospects in HSC, emphasizing advancements from DL models to the increasing adoption of Transformer and Mamba Model architectures. We systematically review key concepts, methodologies, and state-of-the-art approaches in DL for HSC. Furthermore, we investigate the potential of Transformer-based models and the Mamba Model in HSC, detailing their advantages and challenges. Emerging trends in HSC are explored, including in-depth discussions on Explainable AI and Interoperability concepts, alongside Diffusion Models for image denoising, feature extraction, and image fusion. To substantiate the efficacy of various conventional DL models, Transformers, and the Mamba Model, comprehensive experimental results were conducted on three HS datasets, yielding notable accuracies: 99.94% on Pavia University, 99.41% on Indian Pines, and 99.97% on the University of Houston dataset. Additionally, we identify several open challenges and pertinent research questions in the field of HSC. Finally, we outline future research directions and potential applications aimed at enhancing the accuracy and effic

关键词： Convolutional neural networks