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arXiv 2024年

作者： Barua, Arnab Hatzikirou, Haralampos Abe, Sumiyoshi Tata Institute of Fundamental Research Hyderabad500046 India Technische Univesität Dresden Center for Information Services and High Performance Computing N.thnitzer Stra.e 46 P.O. Box: 01062 Dresden Germany Mathematics Department Khalifa University P.O. Box: 127788 Abu Dhabi United Arab Emirates Department of Physics College of Information Science and Engineering Huaqiao University Xiamen361021 China Institute of Physics Kazan Federal University Kazan420008 Russia Department of Natural and Mathematical Sciences Turin Polytechnic University in Tashkent Tashkent100095 Uzbekistan

Learning is a fundamental characteristic of living systems, enabling them to comprehend their environments and make informed decisions. These decision-making processes are inherently influenced by available information about their surroundings and specific objectives. An intriguing perspective is that each process could be optimal under a given set of conditions. Here, a geometric method is introduced for analytically exploring such a perspective. The probability distribution describing the state of the composite system of the environment, termed the information bath, and a decision-maker is discussed on the basis of the entropic concept. This enables one to study the system in analogy with thermodynamics. Learning processes are expressed as the changes of parameters contained in the distribution. For a geometric interpretation of the processes, the manifold endowed with the Fisher-Rao metric as the Riemannian metric is considered. This framework allows one to conceptualize the optimality of each process as a state change along a geodesic curve on the manifold, which is referred to here as geodesic learning. Then, the bivariate Gaussian model is presented for illustrating this approach, and its rigorous solution is obtained. There, the correspondence between learning and cooling is made manifest. Evolution of the entropy is evaluated for geodesic learning. The findings are then interpreted in the context of adaptation. Geodesic learning plays a role of the case of reference and may give novel insights into the mechanics of learning in living systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision making

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A novel tropical geometry-based interpretable machine learning method: Application in prognosis of advanced heart failure

arXiv

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arXiv 2021年

作者： Yao, Heming Derksen, Harm Golbus, Jessica R. Zhang, Justin Aaronson, Keith D. Gryak, Jonathan Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Mathematics Northeastern University BostonMA United States Division of Cardiovascular Medicine Department of Internal Medicine University of Michigan Ann ArborMI United States Electrical and Computer Engineering College of Engineering University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to investigate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, the proposed method was applied to a clinical application that identified heart failure patients that would benefit from advanced therapies such as heart transplant or durable mechanical circulatory support. Experimental results show that the proposed network achieved great performance on the classification tasks. In addition to learning humanly understandable rules from the dataset, existing fuzzy domain knowledge can be easily transferred into the network and used to facilitate model training. From our results, the proposed model and the ability of learning existing domain knowledge can significantly improve the model generalizability. The characteristics of the proposed network make it promising in applications requiring model reliability and justification. Copyright © 2021, The Authors. All rights reserved.

关键词： Domain Knowledge

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Sensor selection for hyper-parameterized linear Bayesian inverse problems

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PAMM 2021年第S1期20卷

作者： Nicole Aretz Peng Chen Karen Veroy Aachen Institute for Advanced Study in Computational Engineering Science (AICES) RWTH Aachen University Schinkelstr. 2 52062 Aachen Germany Oden Institute for Computational Engineering and Sciences University of Texas at Austin 201 E 24th St Austin TX 78712 USA Centre for Analysis Scientific Computing and Applications Eindhoven University of Technology Groene Loper 5 5612 AZ Eindhoven The Netherlands

Models of physical processes often depend on parameters, such as material properties or source terms, that are only known with some uncertainty. Measurement data can be used to estimate these parameters and thereby improve the model's credibility. When measurements become expensive, it is important to choose the most informative data. This task becomes even more challenging when the model configurations vary and the data noise is correlated. In this poster we summarize our results in [1] and present an observability coefficient that describes the influence of the sensors on the inverse solution. It can guide optimal sensor selection towards a uniformly good parameter estimate over all admissible model configurations. We propose a sensor selection algorithm that iteratively improves the observability coefficient, and present numerical results for a steady-state heat conduction problem with correlated noise.

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DPA-2: a large atomic model as a multi-task learner

arXiv

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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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Study of tau neutrinos and non-unitary neutrino mixing with the first six detection units of KM3NeT/ORCA

arXiv

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arXiv 2025年

作者： Aiello, S. Albert, A. Alhebsi, A.R. Alshamsi, M. Garre, S. Alves Ambrosone, A. Ameli, F. Andre, M. Aphecetche, L. Ardid, M. Ardid, S. Aublin, J. Bailly-Salins, L. Bardačová, Z. Baret, B. Bariego-Quintana, A. Becherini, Y. Gualandi, F. Benfenati Benhassi, M. Bennani, M. Benoit, D.M. Berbee, E. Bertin, V. Biagi, S. Boettcher, M. Bonanno, D. Bouasla, A.B. Boumaaza, J. Bouta, M. Bouwhuis, M. Bozza, C. Bozza, R.M. Brânzaş, H. Bretaudeau, F. Breuhaus, M. Bruijn, R. Brunner, J. Bruno, R. Buis, E. Buompane, R. Busto, J. Caiffi, B. Calvo, D. Capone, A. Carenini, F. Carretero, V. Cartraud, T. Cecchini, V. Celli, S. Cerisy, L. Chabab, M. Chen, A. Cherubini, S. Chiarusi, T. Circella, M. Clark, R. Cocimano, R. Coelho, J.A.B. Coyle, P. Creusot, A. Wasseige, G. De Decoene, V. Rosso, I. Del Mauro, L.S. Di Palma, I. Di Díaz, A.F. Diego-Tortosa, D. Distefano, C. Donzaud, C. Drakopoulou, E. Drouhin, D. Ducoin, J.-G. Duverne, P. Eberl, T. Eckerová, E. Eddymaoui, A. Eff, M. Bojaddaini, I. El Hedri, S. El Ellajosyula, V. Enzenhöfer, A. Ferrara, G. Filipović, M.D. Filippini, F. Franciotti, D. Fusco, L.A. Gagliardini, S. Gal, T. Méndez, J. García Soto, A. Garcia Oliver, C. Gatius Geißelbrecht, N. Genton, E. Ghaddari, H. Gialanella, L. Gibson, B.K. Giorgio, E. Goos, I. Gracia, R. Guidi, C. Guillon, B. Gutiérrez, M. Haack, C. van Haren, H. Heijboer, A. Hennig, L. Hernández-Rey, J.J. Idrissi, A. Ibnsalih, W. Idrissi Illuminati, G. Joly, D. de Jong, M. de Jong, P. Jung, B.J. Kalaczynski, P. Kikvadze, V. Kistauri, G. Kopper, C. Kouchner, A. Kovalev, Y.Y. Krupa, L. Kueviakoe, V. Kulikovskiy, V. Kvatadze, R. Labalme, M. Lahmann, R. Lamoureux, M. Larosa, G. Lastoria, C. Lazar, J. Lazo, A. Le Stum, S. Lehaut, G. Lemaître, V. Leonora, E. Lessing, N. Levi, G. Clark, M. Lindsey Magnani, F. Majumdar, J. Malerba, L. Mamedov, F. Manfreda, A. Manousakis, A. Marconi, M. Margiotta, A. Marinelli, A. Markou, C. Martin, L. Via Santa Sofia 64 Catania95123 Italy Université de Strasbourg CNRS IPHC UMR 7178 StrasbourgF-67000 France Khalifa University of Science and Technology Department of Physics PO Box 127788 Abu Dhabi United Arab Emirates Aix Marseille Univ CNRS IN2P3 CPPM Marseille France c/Catedrático José Beltrán 2 Valencia Paterna46980 Spain INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy Università di Napoli "Federico II" Dip. Scienze Fisiche "E. Pancini" Complesso Universitario di Monte S. Angelo Via Cintia ed. G Napoli80126 Italy INFN Sezione di Roma Piazzale Aldo Moro 2 Roma00185 Italy Universitat Politècnica de Catalunya Laboratori d’Aplicacions Bioacústiques Centre Tecnològic de Vilanova i la Geltrú Avda. Rambla Exposició s/n Vilanova i la Geltrú08800 Spain Subatech IMT Atlantique IN2P3-CNRS Nantes Université 4 rue Alfred Kastler - La Chantrerie BP Nantes20722 44307 France Universitat Politècnica de València Instituto de Investigación para la Gestión Integrada de las Zonas Costeras C/ Paranimf 1 Gandia46730 Spain Université Paris Cité CNRS Astroparticule et Cosmologie ParisF-75013 France Università di Genova Via Dodecaneso 33 Genova16146 Italy INFN Sezione di Genova Via Dodecaneso 33 Genova16146 Italy LPC CAEN Normandie Univ ENSICAEN UNICAEN CNRS IN2P3 6 boulevard Maréchal Juin Caen14050 France Czech Technical University in Prague Institute of Experimental and Applied Physics Husova 240/5 Prague110 00 Czech Republic Comenius University in Bratislava Department of Nuclear Physics and Biophysics Mlynska dolina F1 Bratislava842 48 Slovakia INFN Sezione di Bologna v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università di Bologna Dipartimento di Fisica e Astronomia v.le C. Berti-Pichat 6/2 Bologna40127 Italy Università degli Studi della Campania"Luigi Vanvitelli" Dipartimento di Matematica e Fisica viale Lincoln 5 Caserta81100 Italy LPC Campus des Cézeaux 24 av

Oscillations of atmospheric muon and electron neutrinos produce tau neutrinos with energies in the GeV range, which can be observed by the ORCA detector of the KM3NeT neutrino telescope in the Mediterranean Sea. First measurements with ORCA6, an early subarray corresponding to about 5% of the final detector, are presented. A sample of 5828 neutrino candidates has been selected from the analysed exposure of 433 kton-years. The ντ normalisation, defined as the ratio between the number of observed and expected tau neutrino events, is measured to be Sτ = 0.48+0−0.335 . This translates into a ντ charged-current cross section measurement of στmeas = (2.5+2−1.68) × 10−38 cm2 nucleon−1 at the median ντ energy of 20.3 GeV. The result is consistent with the measurements of other experiments. In addition, the current limit on the non-unitarity parameter affecting the τ-row of the neutrino mixing matrix was improved, with α33 > 0.95 at the 95% confidence level. Copyright © 2025, The Authors. All rights reserved.

关键词： Neutrons

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Cross-subject Brain Functional Connectivity Analysis for Multi-task Cognitive State Evaluation

arXiv

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arXiv 2024年

作者： Chen, Jun Chen, Anqi Jiang, Bingkun Obaidat, Mohammad S. Li, Ni Zhang, Xinyu School of Electronics and Information Northwestern Polytechnical University Shaanxi Xi’an710072 China School of Electronics and Information Northwestern Polytechnical University Xi’an710072 China Chongqing Institute for Brain and Intelligence Guangyang Bay Laboratory Chongqing400064 China King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India School of Aeronautics Northwestern Polytechnical University Xi’an710072 China

Cognition refers to the function of information perception and processing, which is the fundamental psychological essence of human beings. It is responsible for reasoning and decision-making, while its evaluation is significant for the aviation domain in mitigating potential safety risks. Existing studies tend to use varied methods for cognitive state evaluation yet have limitations in timeliness, generalisation, and interpretability. Accordingly, this study adopts brain functional connectivity with electroencephalography signals to capture associations in brain regions across multiple subjects for evaluating real-time cognitive states. Specifically, a virtual reality-based flight platform is constructed with multi-screen embedded. Three distinctive cognitive tasks are designed and each has three degrees of difficulty. Thirty subjects are acquired for analysis and evaluation. The results are interpreted through different perspectives, including inner-subject and cross-subject for task-wise and gender-wise underlying brain functional connectivity. Additionally, this study incorporates questionnaire-based, task performance-based, and physiological measure-based approaches to fairly label the trials. A multi-class cognitive state evaluation is further conducted with the active brain connections. Benchmarking results demonstrate that the identified brain regions have considerable influences in cognition, with a multi-class accuracy rate of 95.83% surpassing existing studies. The derived findings bring significance to understanding the dynamic relationships among human brain functional regions, cross-subject cognitive behaviours, and decision-making, which have promising practical application values. © 2024, CC BY.

关键词： Electroencephalography

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Overcoming the disconnect between energy system and climate modelling

arXiv

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arXiv 2022年

作者： Craig, Michael T. Wohland, Jan Stoop, Laurens P. Kies, Alexander Pickering, Bryn Bloomfield, Hannah C. Browell, Jethro De Felice, Matteo Dent, Chris J. Deroubaix, Adrien Frischmuth, Felix Gonzalez, Paula L.M. Grochowicz, Aleksander Gruber, Katharina Härtel, Philipp Kittel, Martin Kotzur, Leander Labuhn, Inga Lundquist, Julie K. Pflugradt, Noah van der Wiel, Karin Zeyringer, Marianne Brayshaw, David J. School for Environment and Sustainability University of Michigan United States Institute for Environmental Decisions ETH Zurich Switzerland Helmholtz-Zentrum Hereon Germany Information and Computing Science Copernicus Institute of Sustainable Development Utrecht University Netherlands TenneT TSO B.V. Arnhem Netherlands Royal Netherlands Meteorological Institute Netherlands Frankfurt Institute for Advanced Studies Germany Department of Electrical and Computer Engineering Aarhus University Denmark ETH Zürich Switzerland School of Geographical Sciences University of Bristol United Kingdom Department of Meteorology University of Reading United Kingdom University of Glasgow United Kingdom European Commission Joint Research Centre Netherlands School of Mathematics University of Edinburg United Kingdom The Alan Turing Institute United Kingdom Max Planck Institute Germany Fraunhofer Institute for Energy Economics and Energy System Technology Germany Department of Mathematics University of Oslo Norway University of Natural Resources and Life Sciences Austria German Institute for Economic Research Germany Institute of Energy and Climate Research Techno-economic Systems Analysis Germany University of Bremen Institute of Geography Germany Dept. of Atmospheric and Oceanic Science University of Colorado Boulder United States National Renewable Energy Laboratory United States Renewable and Sustainable Energy Institute United States Forschungszentrum Jülich Germany Department of Technology Systems University of Oslo Norway

Energy system models underpin decisions by energy system planners and operators. Energy system modelling faces a transformation: accounting for changing meteorological conditions imposed by climate change. To enable that transformation, a community of practice in energy-climate modelling has started to form that aims to better integrate energy system models with weather and climate models. Here, we evaluate the disconnects between the energy system and climate modelling communities, then lay out a research agenda to bridge those disconnects. In the near-term, we propose interdisciplinary activities for expediting uptake of future climate data in energy system modelling. In the long-term, we propose a transdisciplinary approach to enable development of (1) energy-system-tailored climate datasets for historical and future meteorological conditions and (2) energy system models that can effectively leverage those datasets. This agenda increases the odds of meeting ambitious climate mitigation goals by systematically capturing and mitigating climate risk in energy sector decision making. © 2022, CC BY-NC-ND.

关键词： Decision making

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Crossover between liquidlike and gaslike behavior in CH4 at 400 K

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Physical Review E 2017年第5期96卷 052113-052113页

作者： D. Smith M. A. Hakeem P. Parisiades H. E. Maynard-Casely D. Foster D. Eden D. J. Bull A. R. L. Marshall A. M. Adawi R. Howie A. Sapelkin V. V. Brazhkin J. E. Proctor Materials and Physics Research Group School of Computing Science and Engineering University of Salford Manchester M5 4WT United Kingdom School of Mathematics and Physical Sciences University of Hull Hull HU6 7RX United Kingdom European Synchrotron Radiation Facility Beamline ID27 Boîte Postale 220 Grenoble France IMPMC Université Pierre et Marie Curie 4 place Jussieu 75005 Paris France Australian Nuclear Science and Technology Organisation Locked Bag 2001 Kirrawee DC New South Wales 2232 Australia SUPA School of Physics and Centre for Science at Extreme Conditions University of Edinburgh Edinburgh EH9 3JZ United Kingdom Center for High Pressure Science & Technology Advanced Research (HPSTAR) Shanghai 201203 People's Republic of China School of Physics and Astronomy Queen Mary University of London London E1 4NS United Kingdom Institute for High Pressure Physics RAS 108440 Troitsk Moscow Russia Photon Science Institute and School of Electrical and Electronic Engineering The University of Manchester Oxford Road Manchester M13 9PL United Kingdom

We report experimental evidence for a crossover between a liquidlike state and a gaslike state in fluid methane (CH4). This crossover is observed in all of our experiments, up to a temperature of 397 K, 2.1 times the critical temperature of methane. The crossover has been characterized with both Raman spectroscopy and x-ray diffraction in a number of separate experiments, and confirmed to be reversible. We associate this crossover with the Frenkel line—a recently hypothesized crossover in dynamic properties of fluids extending to arbitrarily high pressure and temperature, dividing the phase diagram into separate regions where the fluid possesses liquidlike and gaslike properties. On the liquidlike side the Raman-active vibration increases in frequency linearly as pressure is increased, as expected due to the repulsive interaction between adjacent molecules. On the gaslike side this competes with the attractive van der Waals potential leading the vibration frequency to decrease as pressure is increased.

关键词： Critical phenomena Thermodynamics Fluid planets Liquids Raman spectroscopy X-ray diffraction

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Characterization of wetting using topological principles

arXiv

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arXiv 2020年

作者： Sun, Chenhao McClure, James E. Mostaghimi, Peyman Herring, Anna L. Meisenheimer, Douglas E. Wildenschild, Dorthe Berg, Steffen Armstrong, Ryan T. School of Minerals & Energy Resources Engineering University of New South Wales KensingtonNSW2052 Australia Advanced Research Computing Virginia Polytechnic Institute & State University BlacksburgVA24061 United States Department of Applied Mathematics Australian National University CanberraACT2601 Australia Department of Chemical Biological and Environmental Engineering Oregon State University CorvallisOR97331 United States Hydrocarbon Recovery Shell Global Solutions International B.V. Grasweg 31 Amsterdam1031 HW Netherlands Department of Earth Science & Engineering Imperial College London LondonSW7 2AZ United Kingdom Department of Chemical Engineering Imperial College London LondonSW7 2AZ United Kingdom

Hypothesis Understanding wetting behavior is of great importance for natural systems and technological applications. The traditional concept of contact angle, a purely geometrical measure related to curvature, is often used for characterizing the wetting state of a system. It can be determined from Young's equation by applying equilibrium thermodynamics. However, whether contact angle is a representative measure of wetting for systems with significant complexity is unclear. Herein, we hypothesize that topological principles based on the Gauss-Bonnet theorem could yield a robust measure to characterize wetting. Theory and Experiments We introduce a macroscopic contact angle based on the deficit curvature of the fluid interfaces that are imposed by contacts with other immiscible phases. We perform sessile droplet simulations followed by multiphase experiments for porous sintered glass and Bentheimer sandstone to assess the sensitivity and robustness of the topological approach and compare the results to other traditional approaches. Findings We show that the presented topological principle is consistent with thermodynamics under the simplest conditions through a variational analysis. Furthermore, we elucidate that at sufficiently high image resolution the proposed topological approach and local contact angle measurements are comparable. While at lower resolutions, the proposed approach provides more accurate results being robust to resolution-based effects. Overall, the presented concepts open new pathways to characterize the wetting state of complex systems and theoretical developments to study multiphase systems. Copyright © 2020, The Authors. All rights reserved.

关键词： Wetting

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Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

arXiv

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arXiv 2019年

作者： Kuijf, Hugo J. Biesbroek, J. Matthijs Bresser, Jeroen de Heinen, Rutger Andermatt, Simon Bento, Mariana Berseth, Matt Belyaev, Mikhail Cardoso, M. Jorge Casamitjana, Adrià Collins, D. Louis Dadar, Mahsa Georgiou, Achilleas Ghafoorian, Mohsen Jin, Dakai Khademi, April Knight, Jesse Li, Hongwei Lladó, Xavier Luna, Miguel Mahmood, Qaiser McKinley, Richard Mehrtash, Alireza Ourselin, Sébastien Park, Bo-yong Park, Hyunjin Park, Sang Hyun Pezold, Simon Puybareau, Elodie Rittner, Leticia Sudre, Carole H. Valverde, Sergi Vilaplana, Verónica Wiest, Roland Xu, Yongchao Xu, Ziyue Zeng, Guodong Zhang, Jianguo Zheng, Guoyan Chen, Christopher Flier, Wiesje van der Barkhof, Frederik Viergever, Max A. Biessels, Geert Jan Image Sciences Institute UMC Utrecht Utrecht University Netherlands Brain Center Rudolf Magnus UMC Utrecht Utrecht University Netherlands Department of Radiology UMC Utrecht Department of Radiology LUMC Leiden Netherlands Department of Biomedical Engineering University of Basel Allschwil Switzerland Radiology and Clinical Neuroscience Hotchkiss Brain Institute University of Calgary AB Canada NLP Logix Skolkovo Institute of Science and Technology School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical Image Computing University College London Signal Theory and Communications Department Universitat Politècnica de Catalunya BarcelonaTech Barcelona Spain McGill University Canada Computational Statistics and Machine Learning MSc University College London TomTom Amsterdam Netherlands Department of Radiology and Imaging Science National Institutes of Health United States Ryerson University Canada University of Guelph Canada Sun Yat-sen University University of Dundee Technical University of Munich Research institute of Computer Vision and Robotics University of Girona Spain Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology Daegu Korea Republic of Pakistan Institute of Nuclear Science and Technology Support Center for Advanced Neuroimaging Institute for Diagnostic and Interventional Neuroradiology Inselspital University of Bern Switzerland Electrical and Computer Engineering Department University of British Columbia Vancouver Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston School of Biomedical Engineering and Imaging Sciences King’s College London Suwon Korea Republic of School of Electronic and Electrical Engineering Sungkyunkwan University Suwon Korea Republic of France School of Electrical and Computer Engineering University of Campinas SP Brazil School of Biomedical Engineering and Imaging Sciences King’s College London Centre for Medical I

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their method on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge (https://***/).Sixty T1+FLAIR images from three MR scanners were released with manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. Segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: (1) Dice similarity coefficient, (2) modified Hausdorff distance (95th percentile), (3) absolute log-transformed volume difference, (4) sensitivity for detecting individual lesions, and (5) F1-score for individual lesions. Additionally, methods were ranked on their inter-scanner *** participants submitted their method for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all methods generalize to unseen *** challenge remains open for future submissions and provides a public platform for method evaluation. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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