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arXiv 2025年

作者： Cheng, Sibo Bocquet, Marc Ding, Weiping Finn, Tobias Sebastian Fu, Rui Fu, Jinlong Guo, Yike Johnson, Eleda Li, Siyi Liu, Che Moro, Eric Newton Pan, Jie Piggott, Matthew Quilodran, Cesar Sharma, Prakhar Wang, Kun Xiao, Dunhui Xue, Xiao Zeng, Yong Zhang, Mingrui Zhou, Hao Zhu, Kewei Arcucci, Rossella CEREA ENPC EDF R&D Institut Polytechnique de Paris Île-de-France France School of Artificial Intelligence and Computer Science Nantong University Jiangsu Nantong226019 China School of Mathematical Sciences Key Laboratory of Intelligent Computing and Applications Tongji University Shanghai200092 China School of Engineering and Materials Science Faculty of Science and Engineering Queen Mary University of London LondonE1 4NS United Kingdom Zienkiewicz Centre for Modelling Data and AI Faculty of Science and Engineering Swansea University SwanseaSA1 8EN United Kingdom Department of Computer Science and Engineering Hong Kong university of science and technology Hong Kong Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Tianjin Key Laboratory of Imaging and Sensing Microelectronics Technology School of Microelectronics Tianjin University Tianjin300072 China Centre for Health Informatics Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Department of Community Health Sciences Cumming School of Medicine University of Calgary CalgaryABT2N 1N4 Canada Undaunted Grantham Institute for Climate Change and the Environment Imperial College London LondonSW7 2AZ United Kingdom Culham Campus AbingdonOX14 3DB United Kingdom Centre for Computational Science Department of Chemistry University College London LondonWC1E 6BT United Kingdom Concordia Institute for Information Systems Engineering Concordia University MontrealQCH3G 1M8 Canada School of Mechanical Medical and Process Engineering Faculty of Engineering Queensland University of Technology BrisbaneQLD Australia Department of Chemical Engineering University College London LondonWC1E 6BT United Kingdom

Unstructured grid data are essential for modelling complex geometries and dynamics in computational physics. Yet, their inherent irregularity presents significant challenges for conventional machine learning (ML) techniques. This paper provides a comprehensive review of advanced ML methodologies designed to handle unstructured grid data in high-dimensional dynamical systems. key approaches discussed include graph neural networks, transformer models with spatial attention mechanisms, interpolation-integrated ML methods, and meshless techniques such as physics-informed neural networks. These methodologies have proven effective across diverse fields, including fluid dynamics and environmental simulations. This review is intended as a guidebook for computational scientists seeking to apply ML approaches to unstructured grid data in their domains, as well as for ML researchers looking to address challenges in computational physics. It places special focus on how ML methods can overcome the inherent limitations of traditional numerical techniques and, conversely, how insights from computational physics can inform ML development. To support benchmarking, this review also provides a summary of open-access datasets of unstructured grid data in computational physics. Finally, emerging directions such as generative models with unstructured data, reinforcement learning for mesh generation, and hybrid physics-data-driven paradigms are discussed to inspire future advancements in this evolving field. © 2025, CC BY-NC-SA.

关键词： Mesh generation

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A Reliable Data Hiding Scheme Using Jigsaw Sudoku

A Reliable Data Hiding Scheme Using Jigsaw Sudoku

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Wanli Lyu Dongshuo Yin Yu Zhang Jiahao Deng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

A distinctive data hiding method based on Jigsaw Sudoku is proposed in this *** from other methods,the proposed method is based on Jigsaw Sudoku which is a kind of irregularly shaped *** to the novelty of Jigsaw Sudoku,the proposed method can provide a safer scheme for data hiding than that with pure *** proposed method can hide a certain number of 9-ary numbers in a steganographic image which has a corresponding *** a 256*256 key matrix stretched from a size 9*9 Jigsaw Sudoku,secret message can be hidden in a vector image byte by byte in the form of *** results show that,the proposed method has a better peaksignal-to-noise-ratio(PSNR) compared with some other data hiding methods.

关键词： Sudoku PSNR hiding methods irregularly shaped steganographic image corresponding specification

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Tangram Algorithm:High image Quality Secret Sharing Scheme Using Two Steganographic images

Tangram Algorithm:High Image Quality Secret Sharing Scheme U...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Wanli Lyu Jiahao Deng Yu Zhang Dongshuo Yin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

In this paper,our proposed scheme using two steganography *** other data hiding schemes,this scheme studies the characteristics of the Tangram puzzle model and the secret message bytes that make up the eight binary bits that are hidden into the two *** matrix M *,horizontal and vertical coordinate range from 0 to 255,and the pixel pairs in the cover image are used to help hide the secret message bytes in our *** paired pixel points into single and double number hidden in two *** show that the program can hide relatively more pixels in a cover image with less losing image quality.

关键词： steganography images Tangram puzzle matrix M hide relatively scenario

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Biomechanical modeling and computer simulation of the brain during neurosurgery

arXiv

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arXiv 2019年

作者： Miller, K. Joldes, G.R. Bourantas, G. Warfield, S.K. Hyde, D.E. Kikinis, R. Wittek, A. Intelligent Systems for Medicine Laboratory Department of Mechanical Engineering University of Western Australia 35 Stirling Highway PerthWA6009 Australia Computational Radiology Laboratory Department of Radiology Boston Children's Hospital Harvard Medical School 300 Longwood Avenue BostonMA02115 United States Surgical Planning Laboratory Brigham and Women's Hospital Harvard Medical School 45 Francis St BostonMA02115 United States Medical Image Computing University of Bremen Germany Fraunhofer MEVIS Bremen Germany

Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the intelligent Systems for Medicine laboratory and it's collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach, and describe the brain deformation in mechanical terms (such as displacements, strains and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the Total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment. Copyright © 2019, The Authors. All rights reserved.

关键词： Numerical methods

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medical entity recognition of Esophageal Carcinoma based on word clustering

Medical entity recognition of Esophageal Carcinoma based on ...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Wang, Rui Zhao, Jinfeng Peng, Lizhi Yang, Bo Wang, Lin Li, Baosheng University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China Shangdong Medical Imaging and Radiotherapy Engineering Center Jinan250117 China Department of Radiation Oncology Shandong Cancer Hospital Shandong University Jinan250117 China

ISBN: (纸本)9781728105512

Electronic medical Records (EMRs) is the core of medical information system in hospital. EMRs arises from the medical institution, and large amount of clinical data generated every day. Due to the EMRs contains many medical entities and clinical information of the patient, by analyzing and mining the texts data, medical knowledge which closely related to patients or certain diseases can be obtained. In this paper, we ues the EMRs of patients with Esophageal Carcinoma(EC), which include the clinical symptoms of the patients, tests they have undergone, results of the examinations, and the diagnosis and treatment plan. In this paper, word vector training is carried out for large-scale electronic medical record corpus by means of the skip model of word2vec deep learning tool. Then applying the features data to the k-means clustering algorithm to identifying medical entities about EC by word clustering. As the first step of medical knowledge mining, medical entity recognition is the premise of extracting the semantic relationship implied in medical text and structuring EMRs. © 2018 IEEE.

关键词： Semantics

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Detection and Tracking of Multiple Mice Using Part Proposal Networks

arXiv

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arXiv 2019年

作者： Jiang, Zheheng Liu, Zhihua Chen, Long Tong, Lei Zhang, Xiangrong Lan, Xiangyuan Crookes, Danny Yang, Ming-Hsuan Zhou, Huiyu School of Computing and Communication Lancaster University United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University China The Shenzhen NS-Tech Co.Ltd. China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom The School of Engineering University of California at Merced MercedCA United States

The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian-inference Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available dataset in the research community that provides a quantitative test-bed for the part detection and tracking of multiple mice, and we here introduce a new challenging Multi-Mice PartsTrack dataset that is made of complex behaviours. Finally, we evaluate our proposed approach against several baselines on our new datasets, where the results show that our method outperforms the other state-of-the-art approaches in terms of accuracy. We also demonstrate the generalization ability of the proposed approach on tracking zebra and locust. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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Visualizing the neuroanatomical changes in Han Chinese adulthood: A pseudo-longitudinal study based on age-related large-scale statistical Chinese brain atlases

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Brain Science Advances 2019年第2期5卷 106-116页

作者： Lin Shi Peipeng Liang Andy Li Raymond Wong Yishan Luo Kai Liu Lening Li Kuncheng Li Research Center for Medical Image Computing The Chinese University of Hong KongHong Kong 999077China Department of Imaging and Interventional Radiology The Chinese University of Hong KongHong Kong 999077China School of Psychology Beijing Key Laboratory of Learning and CognitionCapital Normal UniversityBeijing 10048China BrainNow Research Institute Shenzhen 518081Guangdong ProvinceChina Shenzhen SmartView MedTech Limited Shenzhen 518081Guangdong ProvinceChina Department of Radiology Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsXuanwu HospitalCapital Medical UniversityBeijing 100053China

Objective:Understanding how brain changes over lifetime provides the basis for new insights into neurophysiology and *** this study,we carried out a pseudo-longitudinal study based on age-related Chinese brain atlases(i.e.,Chinese2020)constructed from large-scale volumetric brain MRI data collected in normal Han Chinese adults at varying ***:In order to quantify the deformation and displacement of brains for each voxel as age increases,optical flow algorithm was employed to compute motion vectors between every two consecutive brain templates of the age-related brain atlas,i.e.,***:Dynamic age-related neuroanatomical changes in a standardized brain space were ***,our results demonstrate that brain inward deformation(mainly due to atrophy)can appear in adulthood and this trend generally accelerates as age increases,affecting multiple regions including frontal cortex,temporal cortex,parietal cortex,and cerebellum,whereas occipital cortex is least affected by aging,and even showed some degree of outward deformation in the ***:Our findings indicated more complicated age-related changes instead of a simple trend of brain volume decrease,which may be in line with the recently increasing interests in the age-related cortical complexity with other morphometry measures.

关键词： age-related statistical brain atlas magnetic resonance imaging pseudo-longitudinal study brain maturation and ageing

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An Early Stage Researcher's Primer on Systems Medicine Terminology

Network and Systems Medicine

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Network and Systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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White Matter Deficits Underlying the Impaired Consciousness Level in Patients with Disorders of Consciousness

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Neuroscience Bulletin 2018年第4期34卷 668-678页

作者： Xuehai Wu Jiaying Zhang Zaixu Cui Weijun Tang Chunhong Shao Jin Hu Jianhong Zhu Yao Zhao Lu Lu Gang Chen Georg Northoff Gaolang Gong Ying Mao Yong He Neurosurgical Department Shanghai Huashan HospitalFudan University State Key Laboratory of Cognitive Neuroscience and Learning IDG/Mc Govern Institute for Brain ResearchBeijing Normal University Centre for Medical Image Computing Department of Computer Science University College London Radiological Department Shanghai Huashan Hospital Fudan University Psychiatry Department Shanghai Huashan Hospital Fudan University Huajia Hospital Shanghai Scientific and Statistical Computing Core National Institute of Mental Health National Institutes of Health Department of Health and Human Services Institute of Mental Health Research University of Ottawa

In this study, we aimed to （1） identify white matter （WM） deficits underlying the consciousness level in patients with disorders of consciousness （DOCs） using diffusion tensor imaging （DTI）, and （2） evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/ vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest （ROIs）, and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.

关键词： Disorder of consciousness White matter Diffusion tensor imaging Brain injury

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A Novel Registration Method of Brain PET images and Talairach Atlas 29

A Novel Registration Method of Brain PET Images and Talairac...

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第29届中国控制与决策会议

作者： Tan Wenjun Kang Ying Sun Xiaohan Guo Chan School of Computer Science and Engineering Northeastern University Key Laboratory of Medical Image Computing of Ministry of Education Northeastern University

ISBN: (纸本)9781509046584

In recent years,the PET as an important clinical examination imaging technology of radionuclide imaging has become the indispensable tool of for cancer and neurological diseases *** the defect of poor resolution and brain structural features are not clear on PET image,it is difficult to select the points accurately when brain PET image register for the brain *** CT image has good spatial resolution and high density *** a novel registration algorithm of PET and standard brain atlas is presented in this paper by transforming PET-CT-atlas multimodality image based on marking the feature points on the CT ***,the brain tissue,the minimum oriented bounding box and the mid-sagital plane are extracted from brain CT ***,the brain cortical landmarks is manually marked from CT images and transformed into PET images with mutual information matrix of PET-CT ***,the automatic registration of PET-Talairach atlas is realized with the spatial affine transformation matrix,which is calculated by using the brain cortical landmarks of PET image and Talairach *** experimental results show that the method of multi modality registration of PET-CT-T atlas greatly reduces the error of manual intervention,and is good accuracy and lower time complexity.

关键词： Talairach atlas PET image Multimodality Brain atlas Registration

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