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SSRN 2024年

作者： Fan, Chao Zhu, Xinru Peng, Bincheng Xuan, Zhihui Zhu, Zhentong School of Artificial Intelligence and Big Data Henan University of Technology Henan Province Zhengzhou City China Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Province Zhengzhou City China School of Information Science and Engineering Henan University of Technology Henan Province Zhengzhou City450001 China

Medical image registration is crucial for tumor growth monitoring, radiation therapy, and disease diagnosis. Recently, U-Net type networks have become widely used in unsupervised image registration to predict dense deformation fields. However, for image data with large deformations, these networks are prone to interference from outliers and task-irrelevant regions, leading to suboptimal registration accuracy and difficulty in maintaining the continuity of the deformation field. To address this issue, we propose a novel model called ADDR-Net. ADDR-Net leverages dual encoders and incorporates a Cross-Perceptual Attention Module (CPA) alongside a Multi-dimensional Attention Module (MDA) to enhance registration. Unlike traditional methods that concatenate fixed and moving images into a single encoder, ADDR-Net uses dual encoders to separately extract features from each image, facilitating explicit feature matching between image pairs. The CPA module, embedded within the encoder, enhances the extraction of global features over long distances. Meanwhile, the MDA module, embedded within the skip connection, filters redundant features and extracts local refined features, thereby improving the U-shaped network architecture. Experimental results demonstrate that ADDR-Net outperforms state-of-the-art methods. Specifically, compared to the Transformer-based TransMorph method, ADDR-Net increases the Dice scores on two publicly available brain datasets, IXI and OASIS, by 1.4% and 3.5%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network architecture

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Intervention Prediction for Patients with Pressure Injury Using Random Forest 12

Intervention Prediction for Patients with Pressure Injury Us...

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12th IEEE International Conference on big knowledge, ICBK 2021

作者： Jin, Liuqi Pan, Yan Yang, Jiaoyun Han, Lin Lv, Lin Raviv, Miki An, Ning Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data of The Ministry of Education School of Computer Science and Information Engineering Hefei China Lanzhou University Evidence-Based Nursing Center School of Nursing Lanzhou China Wound and Ostomy Care Center Gansu Provincal Hospital Lanzhou China Vitalerter LTD Ha-Yarden Airport City Israel

ISBN: (纸本)9781665438582

Pressure injury (PI) is one of the major causes of short-term death. Early intervention for patients at risk plays an essential role in PI. However, many nurses may ignore risks. This paper aims to establish a model to predict interventions according to the patient's physical signs, which can help nurses develop care plans. We used data from 1, 483 patients with 25 characteristics and 17 interventions. We use the Random Forest and Particle Swarm Optimization (PSO) to optimize model parameters. Then we compared it with KNN, SVM, and Decision Tree. The 10-fold cross-validation result showed that the Random Forest has better accuracy than other methods, with an f1 score of 0.84. This finding proved the feasibility of using machine learning to help formulate care plans according to the classification of index prediction results. Our model shows that hemoglobin, Braden PI score, and age are the three most influential risk factors. © 2021 IEEE.

关键词： Decision trees

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Double-Flow GAN model for the reconstruction of perceived faces from brain activities

arXiv

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arXiv 2023年

作者： Wang, Zihao Zhao, Jing Ding, Xuetong Zhang, Hui School of Computer Science and Engineering Beihang University Beijing China School of Engineering Medicine Beihang University Beijing China School of Biological Science and Medical Engineering Beihang University Beijing China Key Laboratory of Biomechanics and Mechanobiology Ministry of Education Beihang University Beijing China Key Laboratory of Big Data-Based Precision Medicine Ministry of Industry and Information Technology of the People’s Republic of China Beihang University Beijing China

Face plays an important role in human’s visual perception, and reconstructing perceived faces from brain activities is challenging because of its difficulty in extracting high-level features and maintaining consistency of multiple face attributes, such as expression, identity, gender, etc. In this study, we proposed a novel reconstruction framework, which we called Double-Flow GAN, that can enhance the capability of discriminator and handle imbalances in images from certain domains that are too easy for generators. We also designed a pretraining process that uses features extracted from images as conditions for making it possible to pretrain the conditional reconstruction model from fMRI in a larger pure image dataset. Moreover, we developed a simple pretrained model for fMRI alignment to alleviate the problem of cross-subject reconstruction due to the variations of brain structure among different subjects. We conducted experiments by using our proposed method and traditional reconstruction models. Results showed that the proposed method is significant at accurately reconstructing multiple face attributes, outperforms the previous reconstruction models, and exhibited state-of-the-art reconstruction abilities. Copyright © 2023, The Authors. All rights reserved.

关键词： Gallium nitride

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The Algorithms for Word Segmentation and Named Entity Recognition of Chinese Medical Records 7th

The Algorithms for Word Segmentation and Named Entity Recogn...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Ye, Yuan-Nong Zheng, Liu-Feng Huang, Meng-Ya Liu, Tao Zeng, Zhu Bioinformatics and Biomedical Big Data Mining Laboratory Department of Medical Informatics School of Big Health Guizhou Medical University Guiyang550025 China Key Laboratory of Environmental Pollution Monitoring and Disease Control Ministry of Education Guizhou Medical University Guiyang550025 China Cells and Antibody Engineering Research Center of Guizhou Province Key Laboratory of Biology and Medical Engineering School of Biology and Engineering Guizhou Medical University Guiyang550025 China

ISBN: (纸本)9783030786144

A complete inpatient electronic medical record contains a lot of information. In recent years, numerous researchers have carried out research on word segmentation of medical texts. Since the medical record text is written by free text, structuring the medical record text is an important part of the medical record intelligent analysis when these medical named entities are recognized. At present, named entity recognition methods are mainly divided into lexicographical and rule-based methods and machine learning-based methods. The machine learn-based approach takes the named entity recognition task as the annotation problem of sequence data, mainly considering the context information. The features commonly used in feature construction are contextual feature and dictionary features. BERT+LSTM+CRF were used to train the named entity recognition model. Open source CRF++ was adopted as the tool we relied on. We trained the LSTM+F model using the results of the original word segmentation and the information in the context as features. We carried out a 5-fold cross validation. The results showed that the overall F-1 score (MICRO-F) of named entity recognition reached 0.92, which confirmed that the model could accurately complete the task of medical named entity recognition. © 2021, Springer Nature Switzerland AG.

关键词： Natural language processing systems

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement 25

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data Science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Chen, Tianhao Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach. © 2023 IEEE.

关键词： Recommender systems

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Social-enhanced recommendation using graph-based contrastive learning 25

Social-enhanced recommendation using graph-based contrastive...

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作者： Xue, Peng Gao, Qian Fan, Jun Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250014 China Qilu University of Technology Shandong Academy of Sciences Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan250014 China China Telecom Digital Intelligence Techonology Co Ltd Shandong Jinan250101 China

ISBN: (纸本)9798350330014

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models. © 2023 IEEE.

关键词： Recommender systems

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Tumor State-Space Network for High and Low Grade Glioma Classification

Tumor State-Space Network for High and Low Grade Glioma Clas...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Qijian Chen Lihui Wang Zeyu Deng Li Wang Chen Ye YueMin Zhu Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 University Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment.

关键词： Deep learning Accuracy Magnetic resonance imaging Signal processing Predictive models Tumors Radiomics

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The Adsorption and Separation of Tungsten and Molybdenum by Chitosan and Chitosan-D301: Experiments and Dft Theoretical Calculation

SSRN

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SSRN 2024年

作者： Chen, Na Ma, Liwen Xi, Xiaoli Nie, Zuoren Collaborative Innovation Center of Capital Resource-Recycling Material Technology College of Materials Science and Engineering Beijing University of Technology Beijing100124 China Key Laboratory of Advanced Functional Materials Ministry of Education College of Materials Science and Engineering Beijing University of Technology Beijing100124 China National Engineering Laboratory for Industrial Big-data Application Technology College of Materials Science and Engineering Beijing University of Technology Beijing100124 China

The adsorption and separation of tungsten and molybdenum are important for efficient recycling of W-Mo secondary resources. The sustainable development of them requires new green and efficient materials. In this paper, Chitosan (CS)-D301 material was obtained by impregnation method, CS and CS-D301 were applied to the adsorption and separation of W and Mo. The adsorption optimization results indicated that W was adsorbed by CS and CS-D301 adsorbents, the separation factor of W/Mo for CS-D301 increased from 21.34 to 26.45compared to CS. The desorption efficiency was still above 94% after five adsorption-desorption cycles. The adsorption energy of the adsorbent was calculated using density functional theory, which belongs to the chemical adsorption behavior. FT-IR, XPS and DFT theoretical calculations indicated that as the surface group N+-R of CS-D301 showed more affinity for W, making it easier to selectively separate W and Mo than CS. It was also found that N is the surface active adsorption site, and belongs to stable chemical adsorption processes. CS-D301 had stronger interaction with W from the Frontier orbital theory. CS and CS-D301 adsorbents were expected to become green adsorbent materials for efficient separation of W and Mo to support resource protection and sustainable development. © 2024, The Authors. All rights reserved.

关键词： Adsorption

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XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Adversarial Network with Explainable AI System for NIR-II Fluorescence Molecular Imaging 22

XAI-PSSGAN: Perception-Enhanced Spectrum Shift Generative Ad...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Fu, Lidan Lu, Binchun Li, Lingbing Shi, Xiaojing Tian, Jie Hu, Zhenhua Cas Key Laboratory of Molecular Imaging Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Department of Precision Instrument Tsinghua University Beijing China Interventional Radiology Department Chinese Pla General Hospital Beijing China Beijing Advanced Innovation Center for Big Data-based Precision Medicine School of Engineering Medicine Beihang University Beijing China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and Technology Xidian University Xi'an China National Key Laboratory of Kidney Diseases Beijing China

ISBN: (纸本)9798331520526

Fluorescence imaging in the second near-infrared window (NIR-II) facilitates the real-time optical contrast for in vivo biomedical imaging. However, the detection noise is an inevitable byproduct of the real-time imaging process. Additionally, fluorescent probes currently approved for clinical use remain limited within the wavelength of NIR-IIa (1000-1300 nm). NIR-IIb (1500-1700 nm) imaging has the potential to deliver higher resolution than NIR-IIa, but relies on toxic fluorescent probes not approved for human use. To support clinical high-precision imaging, we propose a NIR-II fluorescence enhanced imaging method based on perception-enhanced spectrum shift generative adversarial network with explainable AI systems (xAI-PSSGAN). The proposed method tackles the problem of limited unpaired data training, and generates NIR-IIb resembling images with enhanced resolution and reduced noise. Experimental results demonstrate that proposed method effectively improves the imaging quality and surpasses competing alternatives, which is of great significance to promote the high-quality NIR-II imaging in clinical practice. © 2025 IEEE.

关键词： Biomedical image enhancement cycle-consistent generative adversarial network explainability generative AI NIR - II imaging

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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