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CT perfusion parameter estimation in stroke using neural network with transformer and physical model priors

computers in Biology and Medicine 2024年 182卷 109134-109134页

作者： Luo, Luyao Liu, Pan Ye, Wanxing Chen, Fengwei Liu, Yu Liu, Ziyang Jing, Jing Xiong, Yunyun Zhu, Wanlin Jiang, Yong Cheng, Jian Wang, Yongjun Liu, Tao Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China Medical Big Data Research Center Chinese PLA General Hospital Beijing China China National Clinical Research Center for Neurological Diseases Beijing China Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing China School of Computer Science and Engineering Beihang University Beijing China

Objectives: CT perfusion (CTP) imaging is vital in treating acute ischemic stroke by identifying salvageable tissue and the infarcted core. CTP images allow quantitative estimation of CT perfusion parameters, which can provide information on the degree of tissue hypoperfusion and its salvage potential. Traditional methods for estimating perfusion parameters, such as singular value decomposition (SVD) and its variations, are known to be sensitive to noise and inaccuracies in the arterial input function. To our knowledge, there has been no implementation of deep learning methods for CT perfusion parameter estimation. Materials & methods: In this work, we propose a deep learning method based on the Transformer model, named CTPerformer-Net, for CT perfusion parameter estimation. In addition, our method incorporates some physical priors. We integrate physical consistency prior, smoothness prior and the physical model prior through the design of the loss function. We also generate a simulation dataset based on physical model prior for training the network model. Results: In the simulation dataset, CTPerformer-Net exhibits a 23.4 % increase in correlation coefficients, a 95.2 % decrease in system error, and a 90.7 % reduction in random error when contrasted with block-circulant SVD. CTPerformer-Net successfully identifies hypoperfused and infarcted lesions in 103 real CTP images from the ISLES 2018 challenge dataset. It achieves a mean dice score of 0.36 for the infarct core segmentation, which is slightly higher than the commercially available software (dice coefficient: 0.34) used as a reference level by the challenge. Conclusion: Experimental results on the simulation dataset demonstrate that CTPerformer-Net achieves better performance compared to block-circulant SVD. The real-world patient dataset confirms the validity of CTPerformer-Net. © 2024 Elsevier Ltd

关键词： Image segmentation

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PINNs Method for Sloving the Probability Response of the Stochastic Linear System with Fractional Gaussian Noise

PINNs Method for Sloving the Probability Response of the Sto...

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2024 International Conference on Applied Mathematics and Digital Simulation, AMDS 2024

作者： Ma, Shaojuan Li, Baolan Li, Hufei Xiao, Hui School of Mathematics and Information Science North Minzu University Yinchuan 750021 China Ningxia Key Laboratory of Intelligent Information and Big Data Processing Yinchuan 750021 China Ningxia Basic Science Research Center of Mathematics Yinchuan 750021 China Shanghai Institute of Applied Mathematics and Mechanics Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Frontier Science Center of Mechanoinformatics School of Mechanics and Engineering Science Shanghai University Shanghai 200072 China

In this paper, the Physics-informed neural networks (PINNs) algorithm is mainly used to solve the time-dependent Fokker-Planck-Kolmogorov (FPK) equation, and eventually obtain the transient probability density function (PDF). Firstly, we derive the FPK equation for a dynamical system driven by multiplicative fractional Gaussian noise (fGn). Secondly, a deep learning method based on PINNs is introduced for resolving the corresponding time-dependent FPK equation. Finally, one examples under fGn excitation condition is discussed to determine the effectiveness and feasibility of PINNs algorithm. The findings indicate that PINNs algorithm can get the transient solution of the system under multiplicative fGn. Concurrently, using Monte Carlo method technology is employed to assess the accuracy and calculation efficiency of PINNs algorithm, we find good agreement between the different comparison results, confirming that the PINNs algorithm is not only computationally efficient, but also has good validity and interpretability. © Published under licence by IOP Publishing Ltd.

关键词： Dynamical system FPK equation Fractional Gaussian noise Physics-informed neural networks Probability density function

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Mining Limited data Sufficiently: A BERT-inspired Approach for CSI Time Series Application in Wireless Communication and Sensing

arXiv

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arXiv 2024年

作者： Zhao, Zijian Meng, Fanyi Li, Hang Li, Xiaoyang Zhu, Guangxu Shenzhen Research Institute of Big Data Shenzhen518115 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510275 China Shenzhen518115 China

Channel State Information (CSI) is the cornerstone in both wireless communication and sensing systems. In wireless communication systems, CSI provides essential insights into channel conditions, enabling system optimizations like channel compensation and dynamic resource allocation. However, the high computational complexity of CSI estimation algorithms necessitates the development of fast deep learning methods for CSI prediction. In wireless sensing systems, CSI can be leveraged to infer environmental changes, facilitating various functions, including gesture recognition and people identification. Deep learning methods have demonstrated significant advantages over model-based approaches in these fine-grained CSI classification tasks, particularly when classes vary across different scenarios. However, a major challenge in training deep learning networks for wireless systems is the limited availability of data, further complicated by the diverse formats of many public datasets, which hinder integration. Additionally, collecting CSI data can be resource-intensive, requiring considerable time and manpower. To address these challenges, we propose CSI-BERT2 for CSI prediction and classification tasks, effectively utilizing limited data through a pre-training and fine-tuning approach. Building on CSI-BERT1, we enhance the model architecture by introducing an Adaptive Re-Weighting Layer (ARL) and a Multi-Layer Perceptron (MLP) to better capture sub-carrier and timestamp information, effectively addressing the permutation-invariance problem. Furthermore, we propose a Mask Prediction Model (MPM) fine-tuning method to improve the model’s adaptability for CSI prediction tasks. Experimental results demonstrate that CSI-BERT2 achieves state-of-the-art performance across all tasks with relatively fast computation speeds. To facilitate future research, we will make our code and dataset publicly available upon publication. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration

Effective Concurrency Testing for Go via Directional Primiti...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zongze Jiang Ming Wen Yixin Yang Chao Peng Ping Yang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System ByteDance Beijing China School of Computer Science and Technology Huazhong University of Science and Technology China

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.

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Mapping mountain glaciers using an improved U-Net model with cSE

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International Journal of Digital Earth 2022年第1期15卷 463-477页

作者： Suzheng Tian Yusen Dong Ruyi Feng Dong Liang Lizhe Wang School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences Chinese Academy of SciencesBeijingPeople’s Republic of China International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China

Global warming is melting *** in mountain glaciers have a tremendous impact on human *** identification and extraction of glaciers from satellite images are ***,when studying glaciers,materials surrounding the glacier have high spectral similarity to glaciers and are easily misclassified in the identification ***,in this study of glacier extraction,we used an improved U-Net model(a channel-attention U-Net)to map *** model was trained on Landsat 8 Operational Land Imager(OLI)data and a Shuttle Radar Topography Mission(SRTM)digital elevation model(DEM),and was tested on glaciers in the Pamir *** results show that the channel-attention U-Net identifies glaciers with relatively high accuracy compared to U-Net and *** obtained results were fine-tuned by the conditional random field model,effectively reducing background misidentification.

关键词： U-Net channel-attention mechanism conditional random field glacier extraction Pamir Plateau

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Jointly Optimizing the Energy and Time for Multi-UAV 3-D Coverage of Terrestrial Regions

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IEEE Transactions on Mobile Computing 2025年

作者： Gong, Hao Huang, Baoqi Jia, Bing Hao, Lifei Shi, Zhenwei Inner Mongolia University Inner Mongolia Key Laboratory of Wireless Networking and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Hohhot010021 China Inner Mongolia University College of Computer Science Hohhot010021 China Beihang University Image Processing Center School of Astronautics Beijing100191 China

Multi-rotor unmanned aerial vehicles (UAVs) have been widely employed in various sensing tasks, e.g., environmental monitoring and disaster rescuing, many of which often require full coverage of terrestrial regions by UAVs. Efforts have been devoted to minimizing one of two objectives, i.e., energy consumptions and time costs of UAVs fulfilling such tasks, whereas it is still challenging to jointly optimize both objectives due to their complicated interdependent relationship. Therefore, this paper deals with the tasks of sensing terrestrial regions with multiple UAVs, and focuses on the three-dimensional (3-D) coverage problem by formulating a multi-objective optimization problem of jointly minimizing both objectives. Specifically, in order to optimize energy consumption effectively, an advanced closed-form energy consumption model for multi-rotor UAVs is developed based on a rigorous theoretical analysis by introducing the influences of torque and acceleration, which are often ignored by existing heuristic models. Moreover, considering the NP-hardness of the problem, an innovative swarm intelligence optimization framework is established by leveraging a multitasking learning pattern to exploit cross-task knowledge transfer and adopting an improved multi-objective salp swarm algorithm. Therein, two novel operators, i.e., a variable characteristic-guided hybrid solution initialization operator and a large-scale search-space-oriented multi-mechanism solution update operator, are designed to handle continuous, discrete and even high-dimensional variables involved. Real-world experiments validate the proposed energy model due to the reduction of power consumption estimation error by up to 59% compared to baselines, and besides, extensive simulations demonstrate that the proposed algorithm significantly outperforms the benchmarks in terms of both energy consumptions and time costs. © 2002-2012 IEEE.

关键词： Energy utilization

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Exploring Visibility-Based Shading Methods for Graphics and Visualization 15

Exploring Visibility-Based Shading Methods for Graphics and ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Anandhasilambarasan, D. Seth, Kanika Aravind, Ar Goyal, Prakhar Kantharaj, I.K. Ingle, Yashwant S. Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Quantum University Research Center Quantum University India School of Engineering and Technology Mechaical Engineering Karnataka Bangalore India Vishwakarma Institute of Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350370249

Visibility-based shading techniques for laptop snapshots and Visualization can provide realistic and perceptual shading consequences with the aid of utilizing data from the scene's visibility shape. Those methods comprise visibility statistics to compute the approximate illumination of an object or scene, thinking about occlusion consequences from other objects. Visibility-based totally shading algorithms permit more practical actual-time renderings that extra accurately fit the way people perceive the illumination of scenes. Moreover, these techniques offer performance blessings over global illumination-based total methods, such as improved memory burden and faster computation instances, making them appropriate for plenty of applications. Popular visibility-based strategies, including ray casting and direct illumination, are based totally on the idea that each surface is ideally diffuse and homogeneous, which means their absorption and reflection coefficients are constant. To overcome this issue, a range of new techniques integrate visibility and physics-based total fashions, accounting for fabric homes (such as specular highlights) in the illumination computation. This combination of visibility and physics-based fashions can be, in addition, improved via incorporating mild shipping simulation strategies, inclusive of bidirectional reflectance distribution features and course tracing, to capture better and predict the rendering of actual international materials and surfaces. © 2024 IEEE.

关键词： Consequences Perceptual Rendering Shading Utilizing Visualization

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Joint Participation Incentive and Network Pricing Design for Federated Learning 42

Joint Participation Incentive and Network Pricing Design for...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ding, Ningning Gao, Lin Huang, Jianwei Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Harbin Institute of Technology Sch. of Electronics and Info. Eng. and the Guangdong Prov. Key Lab. of Aerosp. Commun. and Networking Technol. Shenzhen China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350334142

Federated learning protects users' data privacy though sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks. © 2023 IEEE.

关键词： Costs

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Zero-shot multi-speaker accent TTS with limited accent data

Zero-shot multi-speaker accent TTS with limited accent data

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mingyang Zhang Yi Zhou Zhizheng Wu Haizhou Li Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore

In this paper, we present a multi-speaker accent speech synthesis framework. It can generate accented speech of unseen speakers using only a limited amount of accent training data. Without relying on the accent lexicon, the proposed network is able to learn the accent phoneme embedding via a simple model adaptation. In specific, a standard multi-speaker speech synthesis is first trained with native speech. Then, an additional neural network module is appended for adaptation to map the native speech to the accented speech. In the experiments, we have synthesized English speech with Singapore and Hindi accents. Both objective and subjective evaluation results successfully confirm that our proposed technique with phoneme mapping is effective to generate high-quality accent speech for unseen speakers.

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Multi-modal and Multi-scale Spatial Environment Understanding for Immersive Visual Text-to-Speech

arXiv

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arXiv 2024年

作者： Liu, Rui He, Shuwei Hu, Yifan Li, Haizhou Inner Mongolia University China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore

Visual Text-to-Speech (VTTS) aims to take the environmental image as the prompt to synthesize the reverberant speech for the spoken content. The challenge of this task lies in understanding the spatial environment from the image. Many attempts have been made to extract global spatial visual information from the RGB space of an spatial image. However, local and depth image information are crucial for understanding the spatial environment, which previous works have ignored. To address the issues, we propose a novel multi-modal and multi-scale spatial environment understanding scheme to achieve immersive VTTS, termed M2SE-VTTS. The multi-modal aims to take both the RGB and Depth spaces of the spatial image to learn more comprehensive spatial information, and the multi-scale seeks to model the local and global spatial knowledge simultaneously. Specifically, we first split the RGB and Depth images into patches and adopt the Gemini-generated environment captions to guide the local spatial understanding. After that, the multi-modal and multi-scale features are integrated by the local-aware global spatial understanding. In this way, M2SE-VTTS effectively models the interactions between local and global spatial contexts in the multi-modal spatial environment. Objective and subjective evaluations suggest that our model outperforms the advanced baselines in environmental speech generation. The code and audio samples are available at: https://***/AI-S2-Lab/M2SE-VTTS. Copyright © 2024, The Authors. All rights reserved.

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