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12th International Conference on Health Information Science, HIS 2023

作者： Zhang, Xingping Zhang, Guijuan Qiu, Xingting Yin, Jiao Tan, Wenjun Yin, Xiaoxia Yang, Hong Wang, Kun Zhang, Yanchun Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China School of Computer Science and Technology Zhejiang Normal University Jinhua321000 China Department of Respiratory and Critical Care First Affiliated Hospital of Gannan Medical University Ganzhou341000 China Institute for Sustainable Industries and Liveable Cities Victoria University Melbourne3011 Australia Department of New Networks Peng Cheng Laboratory Shenzhen518000 China Department of Radiology First Affiliated Hospital of Gannan Medical University Ganzhou341000 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110189 China

ISBN: (纸本)9789819971077

Objectives: Radiomics has a novel value in accurately and noninvasively characterizing non-small cell lung cancer (NSCLC), but the role of peritumoral features has not been discussed in depth. This work aims to systematically assess the additional value of peritumoral features by exploring the impact of peritumoral region size. Materials and methods: A total of 370 NSCLC patients who underwent preoperative contrast-enhanced CT scans between October 2017 and September 2021 were retrospectively analyzed. The study was carefully designed with a radiomics pipeline to predict lymphovascular invasion, pleural invasion, and T4 staging. To assess the impact of peritumoral features, tumor regions of interest (ROIs) annotated by two medical experts were automatically expanded to produce peritumoral ROIs of different regional sizes, with edge thicknesses of 1 mm, 3 mm, 5 mm, and 7 mm. In a custom pipeline, prediction models were constructed using peritumoral features with different margin thicknesses and intratumoral features of the primary tumor. Results: Radiomics features combining intratumoral and peritumoral regions were created based on the best features of each ROI. Models incorporating peritumoral features yielded varying degrees of improvement in AUCs compared to models using only intratumoral features. The choice of peritumoral size may impact the degree of improvement in radiomics analysis. Conclusions: The integration of peritumoral features has shown potential for improving the predictive value of radiomics. However, selecting an appropriate peritumoral region size is constrained by various factors such as clinical issues, imaging modalities, and ROI annotations. Therefore, future radiomics studies should consider these factors and optimize peritumoral features to cater to specific applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Tumors

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Straightness Measurement Based on FOG-IMU and Shearer Motion Constraints for Longwall Coal Mining

Advanced Devices and Instrumentation

引用

Advanced Devices and Instrumentation 2024年 5卷

作者： Shang, Ziqin Chen, Shaohua Li, Guannan Zheng, Yi Dai, Jianping Qiu, Jinbo Hu, Huizhu State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and Engineering Zhejiang University Hangzhou China Hangzhou Applied Acoustics Research Institute Hangzhou China Navigation Research Center College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Research Center for Novel Computing Sensing and Intelligent Processing Zhejiang Lab Hangzhou China State Key Laboratory of Intelligent Coal Mining and Strata Control Shanghai Institute Co. Ltd China Coal Technology & Engineering Group Shanghai China

Straightness measurements of fully mechanized coal faces are a core requirement for intelligent, comprehensive coal mining. In coal mining, using an inertial measurement unit (IMU) assisted by an odometer for straightness measurement is vital for automating longwall mining. IMUs are less vulnerable to dim and harsh underground mining conditions. However, IMU divergence cannot be controlled solely by the forward velocity obtained from the odometer. To address this problem without external sensors, this paper proposes a straightness measurement method based on a fiber optic gyroscope-IMU/odometer and shearer motion constraints. The motion of the shearer is constrained to correct the pitch and roll attitude angle errors in strapdown inertial navigation solution (SINS) attitude solutions. An extended Kalman filter was established to combine the SINS and dead reckoning using the output from the odometer. Finally, the position estimation obtained by Kalman filter fusion was converted into straightness. An experiment based on a custom-built test rail demonstrated that the proposed algorithm effectively improved the accuracy of straightness measurements in multiple cutting cycles. The error of the results in the horizontal plane was reduced by 45% compared to the traditional IMU/odometer method. This study is important in promoting the unmanned and intelligent development of China’s coal industry. © 2024 Ziqin Shang et al.

关键词： Coal industry

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A Planned Radio Quiet Zone around the Ali Observatory

A Planned Radio Quiet Zone around the Ali Observatory

引用

URSI Atlantic Radio Science Conference (URSI AT-RASC)

作者： Haiyan Zhang Yu Wang Hao Hu Shijie Huang Guifang Wu National Astronomical Observatories Chinese Academy of Sciences 20A Datun Road Chaoyang District Beijing China Key Laboratory of Radio Astronomy and Technology Beijing China Hebei Key Laboratory of Radio Astronomy Technology Shijiazhuang Hebei China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Zhongtai Street Yuhang District Hangzhou Zhejiang China China Electric Power Research Institute Co. Ltd 15 Qinghe Xiaoying East Road Beijing China

ISBN: (数字)9789463968102

ISBN: (纸本)9798350360257

Due to its unique location, the Ali Observatory has been a good site for optical, infrared and radio astronomical telescopes. The Ali CMB (Cosmic Microwave Background) Polarization Telescope (AliCPT-1) is now under construction at the Ali Observatory. In order to protect the radio environment for the Ali observatory, a Radio Quiet Zone (RQZ) is planned. Based on the local natural and social environment, the RQZ size of about 30 km is suggested for compromise to meet the economic development and scientific requirements. The further assessment and legislation of this RQZ is ongoing.

关键词： Economics Observatories Optical polarization Legislation Telescopes

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GESH-NET: GRAPH-ENHANCED SPHERICAL HARMONIC CONVOLUTIONAL NETWORKS FOR CORTICAL SURFACE REGISTRATION

arXiv

引用

arXiv 2024年

作者： Zhang, Ruoyu Wang, Lihui Tang, Kun Xu, Jingwen Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence 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 Guiyang550025 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

Cortical surface registration plays a crucial role in coordinating individual cortical functions and anatomical features, serving as a fundamental step in cortical surface analysis. Its aim is to align the anatomical or functional regions of different individuals, which is of great importance for neuroimaging studies across different populations. Currently, cortical surface registration techniques based on classical methods have been well developed. However, a key issue with classical registration methods is that for each pair of images to be registered, it is necessary to search for the optimal transformation in the deformation space according to a specific optimization algorithm until the similarity measure function converges, which cannot meet the requirements of real-time and high-precision in medical image registration. With the spectacular success of deep learning in the field of computer vision, researching cortical surface image registration techniques based on deep learning models has become a new direction. But so far, there are still only a few studies on cortical surface image registration based on deep learning. Moreover, although deep learning methods theoretically have stronger representation capabilities, surpassing the most advanced classical methods in registration accuracy and distortion control remains a challenge. Therefore, to address this challenge, this paper constructs a deep learning model to study the technology of cortical surface image registration. The specific work is as follows: (1) An unsupervised cortical surface registration network based on a multi-scale cascaded structure is designed, and a convolution method based on spherical harmonic transformation is introduced to register cortical surface data. This solves the problem of scale-inflexibility of spherical feature transformation and optimizes the multi-scale registration process. The results show that the proposed network outperforms the other deep learning-based registration m

关键词： Spheres

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation

arXiv

引用

arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we verify for the first time that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. Copyright © 2024, The Authors. All rights reserved.

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Microbiota enterotoxigenic Bacteroides fragilis-secreted BFT-1 promotes breast cancer cell stemness and chemoresistance through its functional receptor NOD1

引用

Protein & Cell 2024年第6期15卷 419-440页

作者： Wei Ma Lu Zhang Weilong Chen Zhaoxia Chang Juchuanli Tu Yuanyuan Qin Yuwen Yao Mengxue Dong Jiajun Ding Siqin Li Fengkai Li Qiaodan Deng Yifei Yang Tingting Feng Fanrong Zhang Xiying Shao Xueyan He Lixing Zhang Guohong Hu Quentin Liu Yi-Zhou Jiang Shu Zhu Zhi Xiao Dan Su Tong Liu Suling Liu Fudan University Shanghai Cancer Center & Institutes of Biomedical Sciences State Key Laboratory of Genetic EngineeringCancer InstitutesDepartment of OncologyKey Laboratory of Breast Cancer in ShanghaiThe Shanghai Key Laboratory of Medical EpigeneticsShanghai Key Laboratory of Radiation OncologyThe International Co-laboratory of Medical Epigenetics and MetabolismMinistry of Science and TechnologyShanghai Medical CollegeFudan UniversityShanghai 200032China Intelligent Pathology Institute and Department of Pathology The First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefei 230071China Department of Thyroid Breast and Vascular SurgeryXijing HospitalFourth Military Medical UniversityXi’an 710032China Institute of Immunology CAS Key Laboratory of Innate Immunity and Chronic DiseaseSchool of Basic Medical SciencesDivision of Life Science and MedicineUniversity of Science and Technology of ChinaHefei 230027China Department of Pathology The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhou 310022China Department of Breast Surgery The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhou 310022China Department of Breast Medical Oncology The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital)Institute of Basic Medicine and Cancer (IBMC)Chinese Academy of SciencesHangzhou 310022China Shanghai Institute of Nutrition and Health University of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South ChinaGuangzhou 510060China Department of Breast Surgery Xiangya HospitalChangsha 410008China Department of Breast Surgery Tumor Hospital of Harbin Medic

Tumor-resident microbiota in breast cancer promotes cancer initiation and malignant ***,targeting microbiota to improve the effects of breast cancer therapy has not been investigated in ***,we evaluated the microbiota composition of breast tumors and found that enterotoxigenic Bacteroides fragilis(ETBF)was highly enriched in the tumors of patients who did not respond to taxane-based neoadjuvant ***,albeit at low biomass,secreted the toxic protein BFT-1 to promote breast cancer cell stemness and *** studies showed that BFT-1 directly bound to NOD1 and stabilized NOD1 ***1 was highly expressed on ALDH+breast cancer stem cells(BCSCs)and cooperated with GAK to phosphorylate NUMB and promote its lysosomal degradation,thereby activating the NOTCH1-HEY1 signaling pathway to increase ***1 inhibition and ETBF clearance increase the chemosensitivity of breast cancer by impairing BCSCs.

关键词： NOD1 breast chemotherapy

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ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling

arXiv

引用

arXiv 2023年

作者： Zhan, Qiugang Tao, Ran Xie, Xiurui Liu, Guisong Zhang, Malu Tang, Huajin Yang, Yang The Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China The Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The College of Computer Science The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China

In recent years, studies on image generation models of spiking neural networks (SNNs) have gained the attention of many researchers. Variational autoencoders (VAEs), as one of the most popular image generation models, have attracted a lot of work exploring their SNN implementation. Due to the constrained binary representation in SNNs, existing SNN VAE methods implicitly construct the latent space by an elaborated autoregressive network and use the network outputs as the sampling variables. However, this unspecified implicit representation of the latent space will increase the difficulty of generating high-quality images and introduce additional network parameters. In this paper, we propose an efficient spiking variational autoencoder (ESVAE) that constructs an interpretable latent space distribution and designs a reparameterizable spiking sampling method. Specifically, we construct the prior and posterior of the latent space as a Poisson distribution using the firing rate of the spiking neurons. Subsequently, we propose a reparameterizable Poisson spiking sampling method, which is free from the additional network. Comprehensive experiments have been conducted, and the experimental results show that the proposed ESVAE outperforms previous SNN VAE methods in reconstructed & generated image quality. In addition, experiments demonstrate that the encoder of ESVAE can retain the original image information more efficiently and is more robust. The source code is available at https://***/QgZhan/ESVAE. Copyright © 2023, The Authors. All rights reserved.

关键词： Poisson distribution

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UNIQUE SOLVABILITY AND ERROR ANALYSIS OF THE LAGRANGE MULTIPLIER APPROACH FOR GRADIENT FLOWS

arXiv

引用

arXiv 2024年

作者： Cheng, Qing Shen, Jie Wang, Cheng Department of Mathematics Tongji University Shanghai200092 China Key Laboratory of Intelligent Computing and Applications Tongji University Ministry of Education China Eastern Institute of Technology Zhejiang Ningbo315200 China Department of Mathematics University of Massachusetts North DartmouthMA02747 United States

The unique solvability and error analysis of the original Lagrange multiplier approach proposed in [8] for gradient flows is studied in this paper. We identify a necessary and sufficient condition that must be satisfied for the nonlinear algebraic equation arising from the original Lagrange multiplier approach to admit a unique solution in the neighborhood of its exact solution, and propose a modified Lagrange multiplier approach so that the computation can continue even if the aforementioned condition is not satisfied. Using Cahn-Hilliard equation as an example, we prove rigorously the unique solvability and establish optimal error estimates of a second-order Lagrange multiplier scheme assuming this condition and that the time step is sufficient small. We also present numerical results to demonstrate that the modified Lagrange multiplier approach is much more robust and can use much larger time step than the original Lagrange multiplier *** Codes 65M70, 65K15, 65N22 © 2024, CC BY-SA.

关键词： Error analysis

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Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning

arXiv

引用

arXiv 2024年

作者： Tang, Hao Lu, Junhao Huang, Guoheng Li, Ming Chen, Xuhang Zhong, Guo Tan, Zhengguang Li, Zinuo School of Computer Science and Technology Guangdong University of Technology Guangdong China School of Computer Science and Engineering Huizhou University Huizhou China Zhejiang Institute of Optoelectronics Jinhua China Zhejiang Key Laboratory of Intelligent Education Technology and Application Jinhua China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Physics Maths and Computing University of Western Australia WA Australia

In Few-Shot Learning (FSL), traditional metric-based approaches often rely on global metrics to compute similarity. However, in natural scenes, the spatial arrangement of key instances is often inconsistent across images. This spatial misalignment can result in mismatched semantic pixels, leading to inaccurate similarity measurements. To address this issue, we propose a novel method called the Layer-Wise Features Metric of Semantic-Pixel Matching (LWFM-SPM) to make finer comparisons. Our method enhances model performance through two key modules: (1) the Layer-Wise Embedding (LWE) Module, which refines the cross-correlation of image pairs to generate well-focused feature maps for each layer;(2)the Semantic-Pixel Matching (SPM) Module, which aligns critical pixels based on semantic embeddings using an assignment algorithm. We conducted extensive experiments to evaluate our method on four widely used few-shot classification benchmarks: miniImageNet, tieredImageNet, CUB-200-2011, and CIFAR-FS. The results indicate that LWFM-SPM achieves competitive performance across these benchmarks. Our code will be publicly available on https://***/Halo2Tang/Code-for-LWFM-SPM. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

SSRN

引用

SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

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