检索结果-内蒙古大学图书馆

Generative Modelling of the Ageing Heart with Cross-Sectional Imaging and Clinical data

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Qiao, Mengyun Basaran, Berke Doga Qiu, Huaqi Wang, Shuo Guo, Yi Wang, Yuanyuan Matthews, Paul M. Rueckert, Daniel Bai, Wenjia Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Uk Dementia Research Institute Imperial College London London United Kingdom Klinikum Rechts der Isar Technical University of Munich Munich Germany Department of Electronic Engineering Fudan University Shanghai China Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Shanghai Key Laboratory of Miccai Shanghai China

Cardiovascular disease, the leading cause of death globally, is an age-related disease. Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression. In this work, we propose a novel conditional generative model to describe the changes of 3D anatomy of the heart during ageing. The proposed model is flexible and allows integration of multiple clinical factors (e.g. age, gender) into the generating process. We train the model on a large-scale cross-sectional dataset of cardiac anatomies and evaluate on both cross-sectional and longitudinal datasets. The model demonstrates excellent performance in predicting the longitudinal evolution of the ageing heart and modelling its data distribution. The codes are available at https://***/MengyunQ/AgeHeart. © 2022, CC BY-NC-ND.

关键词： Heart

Design of Target Behavior Intelligent Analysis System Based on Distributed computing

学校读者我要写书评

暂无评论

Design of Target Behavior Intelligent Analysis System Based ...

2019 IEEE International Conference on Signal, Information and data Processing, ICSIDP 2019

作者： Pan, Xinlong Cheng, Xueqi Yao, Libo Zhu, Zhenqiu DIng, Biao Tang, Tiantian Li, Minbo Liu, Chuanhui Institute of Information Fusion Naval Aviation University Yantai China CAS Key Laboratory of Network Data Science Technology Inistitute of Computing Technology Chinese Academy of Sciences Beijing China School of Software Fudan University Shanghai China

ISBN: (纸本)9781728123455

Through the fusion processing of target data obtained by radar, electronic reconnaissance, space reconnaissance, technical reconnaissance and civil cooperative system in early warning and surveillance system, multidimensional target data including explicit state information such as target attributes, type, position, speed and course could be obtained. The latent potential information, such as regular behavior, intention and threat degree, need to be mastered by human intelligence and experience analysis. It is urgent to use big data mining technology to realize intelligent analysis of target behavior. Based on the analysis of big data related technologies, this paper designs an intelligent target behavior analysis system based on distributed computing, which provides theoretical and technical support for intelligent upgrading of intelligence processing systems and command decision systems. © 2019 IEEE.

关键词： data mining

Efficient uncertainty quantification for Monte Carlo dose calculations using importance (re-)weighting

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Stammer, P. Burigo, L. Jäkel, O. Frank, M. Wahl, N. Karlsruhe Institute of Technology Steinbuch Centre for Computing Karlsruhe Germany German Cancer Research Center-DKFZ Department of Medical Physics in Radiation Oncology Heidelberg Germany HIDSS4Health-Helmholtz Information and Data Science School for Health Karlsruhe/Heidelberg Germany Heidelberg Germany Heidelberg Ion Beam Therapy Center-HIT Department of Medical Physics in Radiation Oncology Heidelberg Germany

The high precision and conformity of intensity-modulated particle therapy (IMPT) comes at the cost of susceptibility to treatment uncertainties in particle range and patient set-up. Dose uncertainty quantification and mitigation, which is usually based on sampled error scenarios, however becomes challenging when computing the dose with computationally expensive but accurate Monte Carlo (MC) simulations. This paper introduces an importance (re-)weighting method in MC history scoring to concurrently construct estimates for error scenarios, the expected dose and its variance from a single set of MC simulated particle histories. The approach relies on a multivariate Gaussian input and uncertainty model, which assigns probabilities to the initial phase space sample, enabling the use of different correlation models. Exploring and adapting bivariate emittance parametrizations for the beam shape, accuracy can be traded between that of the uncertainty or the nominal dose estimate. The method was implemented using the MC code TOPAS and tested for proton IMPT plan delivery in comparison to a reference scenario estimate. We achieve accurate results for set-up uncertainties (γ3mm=3% ≥ 99:99 %) and expectedly lower but still sufficient agreement for range uncertainties, which are approximated with uncertainty over the energy distribution (γ3mm=3% ≥ 99:50% (E[d]), γ3mm=3% ≥ 91:69% (σ(d)) ). Initial experiments on a water phantom, a prostate and a liver case show that the re-weighting approach lowers the CPU time by more than an order of magnitude. Further, we show that uncertainty induced by interplay and other dynamic inuences may be approximated using suitable error correlation models. © 2021, CC BY.

关键词： Importance sampling

Scientific discovery in the age of artificial intelligence

学校读者我要写书评

暂无评论

NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

Sparse correspondence analysis for contingency tables

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Liu, Ruiping Niang, Ndeye Saporta, Gilbert Wang, Huiwen Beijing Information Science & Technology University Beihang University Beijing China CEDRIC Lab CNAM Paris France School of Economics and Management Beijing Advanced Innovation Center for Big Data & Brain Computing Beihang University Beijing China

Since the introduction of the lasso in regression, various sparse methods have been developed in an unsupervised context like sparse principal component analysis (s-PCA), sparse canonical correlation analysis (s-CCA) and sparse singular value decomposition (s-SVD). These sparse methods combine feature selection and dimension reduction. One advantage of s-PCA is to simplify the interpretation of the (pseudo) principal components since each one is expressed as a linear combination of a small number of variables. The disadvantages lie on the one hand in the difficulty of choosing the number of non-zero coefficients in the absence of a well established criterion and on the other hand in the loss of orthogonality for the components and/or the loadings. In this paper we propose sparse variants of correspondence analysis (CA) for large contingency tables like documents-terms matrices used in text mining, together with pPMD, a deflation technique derived from projected deflation in s-PCA. We use the fact that CA is a double weighted PCA (for rows and columns) or a weighted SVD, as well as a canonical correlation analysis of indicator variables. Applying s-CCA or s-SVD allows to sparsify both rows and columns weights. The user may tune the level of sparsity of rows and columns and optimize it according to some criterium, and even decide that no sparsity is needed for rows (or columns) by relaxing one sparsity constraint. The latter is equivalent to apply s-PCA to matrices of row (or column) profiles. © 2020, CC BY.

关键词： Singular value decomposition

A Generative Shape Compositional Framework to Synthesise Populations of Virtual Chimaeras

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Dou, Haoran Virtanen, Seppo Ravikumar, Nishant Frangi, Alejandro F. The Center for Computational Imaging and Simulation Technologies in Biomedicine within the School of Computing The University of Leeds LeedsLS2 9JT United Kingdom The Christabel Pankhurst Institute Division of Informatics Imaging and Data Sciences University of Manchester ManchesterM1 3BB United Kingdom The Department of Computer Science University of Manchester ManchesterM1 3BB United Kingdom Departments of Electrical Engineering and Cardiovascular Sciences KU Leuven Leuven Belgium Alan Turing Institute London United Kingdom

Generating virtual organ populations that capture sufficient variability while remaining plausible is essential to conduct in-silico trials of medical devices. However, not all anatomical shapes of interest are always available for each individual in a population. The imaging examinations and modalities used can vary between subjects depending on their individualised clinical pathways. Different imaging modalities may have various fields of view, are sensitive to signals from other tissues/organs, or both. Hence, missing/partially overlapping anatomical information is often available across individuals. We introduce a generative shape model for multipart anatomical structures, learnable from sets of unpaired datasets, i.e., where each substructure in the shape assembly comes from datasets with missing or partially overlapping substructures from disjoint subjects of the same population. The proposed generative model can synthesise complete multipart shape assemblies coined virtual chimaeras. We applied this framework to build virtual chimaeras from databases of whole heart shape assemblies that each contribute samples for heart substructures. Specifically, we propose a graph neural network-based generative shape compositional framework which comprises two components, a part-aware generative shape model which captures the variability in shape observed for each structure of interest in the training population, and a spatial composition network which assembles/composes the structures synthesised by the former into multipart shape assemblies (viz. virtual chimaeras). We also propose a novel self-supervised learning scheme that enables the spatial composition network to be trained with partially overlapping data and weak labels. We trained and validated our approach using shapes of cardiac structures derived from cardiac magnetic resonance images in the UK Biobank. When trained with complete and partially overlapping data, our approach significantly outperforms a PCA-base

关键词： Magnetic resonance

Modulus-Based Matrix Splitting Iteration Methods for a Class of Stochastic Linear Complementarity Problem

学校读者我要写书评

暂无评论

American Journal of Operations Research 2019年第6期9卷 245-254页

作者： Qianqian Lu Chenliang Li School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

For the expected value formulation of stochastic linear complementarity problem, we establish modulus-based matrix splitting iteration methods. The convergence of the new methods is discussed when the coefficient matrix is a positive definite matrix or a positive semi-definite matrix, respectively. The advantages of the new methods are that they can solve the large scale stochastic linear complementarity problem, and spend less computational time. Numerical results show that the new methods are efficient and suitable for solving the large scale problems.

关键词： Stochastic Linear Complementarity Problem Modulus-Based Matrix Splitting Expected Value Formulation Positive Semi-Definite Matrix

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

学校读者我要写书评

暂无评论

IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

Interacting dark energy constraints from the full-shape analyses of BOSS DR12 and DES Year 3 measurements

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Tsedrik, M. Lee, S. Markovic, K. Carrilho, P. Pourtsidou, A. Moretti, C. Bose, B. Huff, E. Robertson, A. Taylor, P.L. Zuntz, J. Institute for Astronomy University of Edinburgh Royal Observatory Blackford Hill EdinburghEH9 3HJ United Kingdom Higgs Centre for Theoretical Physics School of Physics and Astronomy EdinburghEH9 3FD United Kingdom Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States SISSA - International School for Advanced Studies Via Bonomea 265 Trieste34136 Italy Centro Nazionale ‘High Performance Computer Big Data and Quantum Computing’ Italy INAF Osservatorio Astronomico di Trieste Via Tiepolo 11 TriesteI-34143 Italy Observatories Carnegie Institution for Science 813 Santa Barbara Street PasadenaCA91101 United States The Ohio State University ColumbusOH43210 United States Department of Physics The Ohio State University ColumbusOH43210 United States Department of Astronomy The Ohio State University ColumbusOH43210 United States

Dark Scattering (DS) is an interacting dark energy model characterised by pure momentum exchange between dark energy and dark matter. It is phenomenologically interesting because it is unconstrained by CMB data and can alleviate the S8 tension. We derive constraints on cosmological and DS parameters using three two-point correlation functions (3×2pt) from the Dark Energy Survey third year data release (DES Y3). We then add information from the multipoles of the galaxy power spectrum combined with Baryonic Acoustic Oscillation (BAO) measurements using the twelfth data release of the Baryon Oscillation Spectroscopic Survey (BOSS DR12) and external BAO measurements. We compare results from the direct combination of the probes with the joint posterior distribution calculated with a normalising flow approach. Additionally, we run a CMB analysis with the Planck Public Release 4 (PR4) for comparison of the cosmological constraints. Overall, we find that the combination of probes allows minimising the projection effects and improves constraints without the need to include CMB information. It brings the marginalised posterior maxima closer to the corresponding best-fit values and weakens the sensitivity to the priors of the spectroscopic modelling nuisance parameters. These findings are highly relevant in light of forthcoming data of surveys like DESI, Euclid, and Rubin. © 2025, CC BY.

关键词： Dark energy