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5th International Symposium on Artificial Intelligence for Medicine science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction 37

HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs f...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Zhang, Xianfeng Gu, Yanhui Xu, Guandong Li, Yafei Wang, Jinlan Yang, Zhenglu School of Computer and Electronic Information Science Nanjing Normal University Nanjing China School of Computer Science and Advanced Analytics Institute University of Technology Sydney Sydeny Australia School of Chemistry and Materials Science Nanjing Normal University Nanjing China School of Physics Southeast University Nanjing China College of Computer Science Nankai University Tianjin China

ISBN: (纸本)9781577358800

Gathering information from multi-perspective graphs is an essential issue for many applications especially for protein-ligand binding affinity prediction. Most of traditional approaches obtained such information individually with low interpretability. In this paper, we harness the rich information from multi-perspective graphs with a general model, which abstractly represents protein-ligand complexes with better interpretability while achieving excellent predictive performance. In addition, we specially analyze the protein-ligand binding affinity problem, taking into account the heterogeneity of proteins and ligands. Experimental evaluations demonstrate the effectiveness of our data representation strategy on public datasets by fusing information from different perspectives. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ligands

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Bayesian Subspace Signal Detection in Partially Homogeneous Environment with Insufficient Training data 3

Bayesian Subspace Signal Detection in Partially Homogeneous ...

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3rd International Conference on Frontiers of electronics, Information and Computation Technologies, ICFEICT 2023

作者： Zhou, Zhe Wu, Yuntao Liu, Weijian Zheng, Daikun Gong, Pengcheng Wuhan Institute of Technology School of Computer Science and Engineering Wuhan China Wuhan Electronic Information Institute Wuhan China

ISBN: (纸本)9798350302356

Partially homogeneous environment (PHE) is a typical non-homogeneity that radar systems frequently experience. Because there are generally insufficient training data to generate a reliable estimate for the unknown covariance matrix, signal detection in PHE can be challenging. In order to address this issue, we apply Bayesian theory in this paper by assuming that an inverse Wishart distribution governs the noise covariance matrix. Subsequently, we introduce an efficient Bayesian detector. The results of numerical examples demonstrate that the proposed detector has significantly better detection performance. © 2023 IEEE.

关键词： Signal detection

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Towards Privacy-Aware and Trustworthy data Sharing Using Blockchain for Edge Intelligence

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Big data Mining and analytics 2023年第4期6卷 443-464页

作者： Youyang Qu Lichuan Ma Wenjie Ye Xuemeng Zhai Shui Yu Yunfeng Li David Smith with Data61 Commonwealth Scientific and Industrial Research Organization(CSIRO)Sydney 2015Australia School of Cyber Engineering Xidian UniversityXi’an 710126China the College of Engineering and Science Victoria UniversityMelbourne 3000Australia School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 610054China School of Computer Science University of Technology SydneySydney 2007Australia CNPIEC KEXIN LTD. Beijing 100020China.

The popularization of intelligent healthcare devices and big data analytics significantly boosts the development of Smart Healthcare Networks(SHNs).To enhance the precision of diagnosis,different participants in SHNs share health data that contain sensitive ***,the data exchange process raises privacy concerns,especially when the integration of health data from multiple sources(linkage attack)results in further *** attack is a type of dominant attack in the privacy domain,which can leverage various data sources for private data ***,adversaries launch poisoning attacks to falsify the health data,which leads to misdiagnosing or even physical *** protect private health data,we propose a personalized differential privacy model based on the trust levels among *** trust is evaluated by a defined community density,while the corresponding privacy protection level is mapped to controllable randomized noise constrained by differential *** avoid linkage attacks in personalized differential privacy,we design a noise correlation decoupling mechanism using a Markov stochastic *** addition,we build the community model on a blockchain,which can mitigate the risk of poisoning attacks during differentially private data transmission over *** experiments and analysis on real-world datasets have testified the proposed model,and achieved better performance compared with existing research from perspectives of privacy protection and effectiveness.

关键词： edge intelligence blockchain personalized privacy preservation differential privacy Smart Healthcare Networks(SHNs)

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Face Attribute Analysis Method Based on Self-Supervised Siamese Network 2022

Face Attribute Analysis Method Based on Self-Supervised Siam...

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14th International Conference on Machine Learning and Computing, ICMLC 2022

作者： Xiong, Huan Dong, Shuai Zou, Kun School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Computer Engineering Zhongshan Institute University of Electronic Science and Technology of China China

ISBN: (纸本)9781450395700

Face attribute analysis, which is a challenging and popular task in the vision field, has been widely used in various fields including intelligent security, human-computer interaction, and targeted promotion. However, in practical applications, people are not always facing the camera, and their head postures would affect the accuracy of attribute prediction. In addition, some attributes are inherently more difficult to predict due to the image noise, motion blur, and light variation. To improve the accuracy of face attribute analysis, a self-supervised Siamese multi-task convolutional neural network (SS-MCNN) is proposed in this paper. First, a Siamese network model is built for multi-task joint training. Second, a sparse self-supervised loss function is designed to learn the common features of Siamese contrastive data. Finally, the proposed SS-MCNN improves the performance on 40 face attributes analysis with an average accuracy of 91.42% on CelebA dataset. For age estimation task, the model also achieves good results with a mean absolute error (MAE) of 3.29 and 3.10 on the AFAD and MORPH II test sets. © 2022 ACM.

关键词： Convolutional neural networks

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Integrated Simulation Research of Multi-natural Energy-driven Unmanned Surface Vehicle

Integrated Simulation Research of Multi-natural Energy-drive...

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2023 International Conference on Applied Mathematics and Digital Simulation, AMDS 2023

作者： Chen, Ao Chen, Tianding He, Hong Wang, Hui Fan, Yimei Yu, Xin School of Physics and Information Engineering Minnan Normal University Fujian Province China School of Computing and Information Science Fuzhou Institute of Technology Fujian Province China School of Data and Computer Science Xiamen Institute of Technology Fujian Province China

In response to the limitations of existing unmanned surface vehicles in complex marine environments, where they often fail to meet the requirements for extended endurance, broad operational range, offshore missions, and prolonged operational durations, an autonomous Unmanned Surface Vehicle (NUSV) has been designed and developed. The NUSV is propelled by wind, solar, and wave energy sources. Concerning wind energy, a micro wind energy storage mechanism based on a spiral spring has been designed to enable power generation in low-wind conditions. A wave energy transmission device, complete with a dynamic model, has been devised for wave energy. This device converts torque generated from wave action in different directions into a unified torque, providing continuous power to drive the generator. Solar energy is harnessed using Maximum Power Point Tracking (MPPT) algorithms to ensure optimal operation of the solar panels. The electrical energy generated from these three natural sources is stored using Pulse Width Modulation (PWM) and DC-DC Boost converters. A programmable logic controller manages and allocates this stored electrical energy for the NUSV's equipment, including underwater propulsion systems, surface cameras, laser radar, GPS, and other electronic devices. This integration of wind, solar, and wave energy sources enables the NUSV to meet power demands, allowing for long-duration operations at sea, extended offshore missions, wide operational ranges, and prolonged mission durations. © Published under licence by IOP Publishing Ltd.

关键词： DC-DC converters

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MSIsensor-RNA:Microsatellite Instability Detection for Bulk and Single-cell Gene Expression data

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Genomics, Proteomics & Bioinformatics 2024年第3期22卷 85-90页

作者： Peng Jia Xuanhao Yang Xiaofei Yang Tingjie Wang Yu Xu Kai Ye Department of Gynecology and Obstetrics Center for Mathematical MedicalThe First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an 710049China Genome Institute The First Affiliated Hospital of Xi’an Jiaotong UniversityXi’an 710061China School of Life Science and Technology Xi’an Jiaotong UniversityXi’an 710049China

Microsatellite instability(MSI)is an indispensable biomarker in cancer ***,MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI ***,the MSI detection method on expression data,especially single-cell expression data,is still lacking,limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell ***,we developed MSIsensor-RNA,an accurate,robust,adaptable,and standalone software to detect MSI status based on expression values of MSI-associated *** demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing(RNA-seq),microarray,and single-cell ***-RNA is a versatile,efficient,and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and ***-RNA is available at https://***/xjtu-omics/msisensor-rna.

关键词： Microsatellite instability Gene expression Single-cell RNA-seq RNA-seq Microarray

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain Systems

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IEEE Transactions on Knowledge and data engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2023年第10期20卷 70-84页

作者： Zhongjie Li Weijie Yuan Qinghua Guo Nan Wu Ji Zhang Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular *** this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional *** first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed *** refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)***,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： orthogonal time frequency space(OTFS) channel estimation hidden Markov model(HMM) unitary approximate message passing(UAMP)

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The Large Language Models on Biomedical data Analysis: A Survey

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4486-4497页

作者： Lan, Wei Tang, Zhentao Liu, Mingyang Chen, Qingfeng Peng, Wei Chen, Yiping Phoebe Pan, Yi Guangxi University School of Computer Electronic and Information Nanning530004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, biomedical researchers currently have limited knowledge about LLM. Therefore, there is an urgent need for a summary of LLM applications in biomedical data analysis. Herein, we propose this review by summarizing the latest research work on LLM in biomedicine. In this review, LLM techniques are first outlined. We then discuss biomedical datasets and frameworks for biomedical data analysis, followed by a detailed analysis of LLM applications in genomics, proteomics, transcriptomics, radiomics, single-cell analysis, medical texts and drug discovery. Finally, the challenges of LLM in biomedical data analysis are discussed. In summary, this review is intended for researchers interested in LLM technology and aims to help them understand and apply LLM in biomedical data analysis research. © 2013 IEEE.

关键词： data consistency

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