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Research Square 2021年

作者： Ding, Liang Zhou, Ruyi Yu, Tianyi Gao, Haibo Yang, Huaiguang Li, Jian Yuan, Ye Liu, Chuankai Wang, Jia Zhao, Yuyan Wang, Zhengyin Wang, Xiyu Bao, Gang Deng, Zongquan Huang, Lan Li, Nan Cui, Xiaofeng He, Ximing Jia, Yang Yuan, Baofeng Liu, Guangjun Zhang, Hui Zhao, Rui Zhang, Zuoyu Cheng, Ziqing Wu, Fan Xu, Qian Lu, Hao Richter, Lutz Liu, Zhen Niu, Fuliang Qi, Huanan Li, Shu Feng, Wenhao Yang, Chaojie Chen, Baichao Dang, Zhaolong Zhang, Mingming Li, Lichun Wang, Xiaoxue Huang, Zhao Zhang, Jitao Xing, Hongjun Wang, Guanyu Niu, Lizhou Xu, Peng Wan, Wenhui Di, Kaichang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China Beijing Aerospace Control Center Beijing100094 China Center for Lunar and Planetary Sciences Institute of Geochemistry Chinese Academy of Science Guiyang550081 China CAS Center for Excellence in Comparative Planetology Hefei230026 China University of Chinese Academy of Sciences Beijing100049 China Beijing Institute of Spacecraft System Engineering Beijing100094 China Department of Aerospace Engineering Ryerson University TorontoONM5B 2K3 Canada Hauptstrasse 1e Eching Germany State Key Laboratory of Remote Sensing Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100101 China

China’s first Mars rover, Zhurong, has successfully touched down on the southern Utopia Planitia of Mars at 109.925° E, 25.066° N, and since performed cooperative multiscale investigations with the Tianwen-1 orbiter. Here we present primary localization and surface characterization results based on complementary data of the first 60 sols. The Zhurong rover has traversed 450.9 m southwards over a flat surface with mild wheel slippage (less than 0.2 in slip ratio). The encountered crescent-shaped sand dune indicates a NE-SW local wind direction, consistent with larger-range remote-sensing observations. Soil parameter analysis based on terramechanics indicates that the topsoil has high bearing strength and cohesion, and its equivalent stiffness and internal friction angle are ~1390-5872 kPa∙m-n and ~21°-34° respectively. Rocks observed strewn with dense pits, or showing layered and flaky structures, are presumed to be involved in physical weathering like severe wind erosion and potential chemical weathering processes. These preliminary observations suggest great potential of in-situ investigations by the scientific payload suite of the Zhurong rover in obtaining new clues of the region’s aeolian and aqueous history. Cooperative investigations using the related payloads on both the rover and the obiter could peek into the habitability evolution of the northern lowlands on Mars. © 2021, CC BY.

关键词： Sols

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Contrast detection is enhanced by deterministic, high-frequency transcranial alternating current stimulation with triangle and sine waveform.

arXiv

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arXiv 2023年

作者： Potok, Weronika van der Groen, Onno Sivachelvam, Sahana Bächinger, Marc Fröhlich, Flavio Kish, Laszlo B. Wenderoth, Nicole Neural Control of Movement Lab Department of Health Sciences and Technology Zurich8093 Switzerland University of Zurich Federal Institute of Technology Zurich University and Balgrist Hospital Zurich Zurich8057 Switzerland 138602 Singapore Neurorehabilitation and Robotics Laboratory School of Medical and Health Sciences Edith Cowan University Joondalup6027 Australia Department of Psychiatry University of North Carolina at Chapel Hill Chapel HillNC United States Carolina Center for Neurostimulation University of North Carolina at Chapel Hill Chapel HillNC United States Department of Neurology University of North Carolina at Chapel Hill Chapel HillNC United States Department of Cell Biology and Physiology University of North Carolina at Chapel Hill Chapel HillNC United States Department of Biomedical Engineering University of North Carolina at Chapel Hill Chapel HillNC United States Neuroscience Center University of North Carolina at Chapel Hill Chapel HillNC United States Department of Electrical & Computer Engineering Texas A&M University TAMUS 3128 College StationTX77843-3128 United States

Stochastic Resonance (SR) describes a phenomenon where an additive noise (stochastic carrier-wave) enhances the signal transmission in a nonlinear system. In the nervous system, nonlinear properties are present from the level of single ion channels all the way to perception and appear to support the emergence of SR. For example, SR has been repeatedly demonstrated for visual detection tasks, also by adding noise directly to cortical areas via transcranial random noise stimulation (tRNS). When dealing with nonlinear physical systems, it has been suggested that resonance can be induced not only by adding stochastic signals (i.e., noise) but also by adding a large class of signals that are not stochastic in nature which cause "deterministic amplitude resonance" (DAR). Here we mathematically show that high-frequency, deterministic, periodic signals can yield resonance-like effects with linear transfer and infinite signal-to-noise ratio at the output. We tested this prediction empirically and investigated whether non-random, high-frequency, transcranial alternating current stimulation applied to visual cortex could induce resonance-like effects and enhance performance of a visual detection task. We demonstrated in 28 participants that applying 80 Hz triangular-waves or sine-waves with tACS reduced visual contrast detection threshold for optimal brain stimulation intensities. The influence of tACS on contrast sensitivity was equally effective to tRNS-induced modulation, demonstrating that both tACS and tRNS can reduce contrast detection thresholds. Our findings suggest that a resonance-like mechanism can also emerge when deterministic electrical waveforms are applied via tACS. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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The design of 1553b relay circuit based on FPGA

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IOP Conference Series: Materials Science and engineering 2020年第1期740卷

作者： Zhonghua Han Chengcheng Huang Jiayu Peng Shuai Wang Faculty of Information and Control Engineering Shenyang Jianzhu University Shenyang 110168 China Department of Digital Factory Shenyang Institute of Automation the Chinese Academy of Sciences (CAS) Shenyang 110016 China Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang 110016 Liaoning China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110016 Liaoning China

On the basis of increasing the transmission distance of the 1553B bus, in order to maintain the simplicity and stability of the bus and improve its anti-jamming ability, the 1553B signal relay circuit based on FPGA is designed in this paper. The signal transmission part is consists of the software and the hardware, and the chip of ZYNQ series is used in the FPGA. Firstly, the transmitted signal oscillation is transformed into a group of positive and negative differential signals, which are realized by transformer (pmdb25). Secondly, the signal is sent to the main control chip of ZYNQ series through signal acquisition chip and level conversion chip. Finally, the data is processed by software program and forwarded to the receiver. The specific experiments have been done on the 1553B signal transmission platform, which significantly show that the proposed scheme is reasonable and reliable.

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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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Editorial Special issue on approximate dynamic programming and reinforcement learning

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控制理论与应用（英文版） 2011年第3期 309-309页

作者： Silvia Ferrari Jagannathan Sarangapani Frank L. Lewis Laboratory for Intelligent Systems and Control(LISC)Department of Mechanical Engineering & Materials Science Box 90300 Duke University Durham NC 27708-0005 Department of Electrical & Computer Engineering University of Missouri-Rolla MO 65401 U.S.A. Automation and Robotics Research Institute The University of Texas at Arlington 7300 Jack Newell Blvd. S Ft. Worth Texas 76118-7115 U.S.A.

We are extremely pleased to present this special issue of the Journal of control Theory and *** dynamic programming (ADP) is a general and effective approach for solving optimal control and estimation problems by adapting to uncertain environments over *** optimizes the sensing objectives accrued over a future time interval with respect to an adaptive control law,conditioned on prior knowledge of the system,its state,and uncertainties.A numerical search over the present value of the control minimizes a Hamilton-Jacobi-Bellman (HJB) equation providing a basis for real-time,approximate optimal control.

关键词： reinforcement learning dynamic programming optimal control effective approach prior knowledge control Theory real-time control law problems special search future ADP basis

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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New results on parameter estimation via dynamic regressor extension and mixing: Continuous and discrete-time cases

arXiv

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arXiv 2019年

作者： Ortega, Romeo Aranovskiy, Stanislav Pyrkin, Anton A. Astolfi, Alessandro Bobtsov, Alexey A. Laboratoire des Signaux et Systèmes CNRS-SUPELEC Plateau du Moulon Gif-sur-Yvette91192 France ITMO University Kronverkskiy av. 49 St. Petersburg197101 Russia CentraleSupélec - IETR Avenue de la Boulaie Cesson-Sévigné35576 France Hangzhou Dianzi University Hangzhou310018 China Faculty of Control Systems and Robotics ITMO University Kronverkskiy av. 49 St. Petersburg197101 Russia Department of Electrical and Electronic Engineering Imperial College London LondonSW7 2AZ United Kingdom DICII Universita di Roma "Tor Vergata" Via del Politecnico 1 Roma00133 Italy

We present some new results on the dynamic regressor extension and mixing parameter estimators for linear regression models recently proposed in the literature. This technique has proven instrumental in the solution of several open problems in system identification and adaptive control. The new results include: (i) a unified treatment of the continuous and the discrete-time cases;(ii) the proposal of two new extended regressor matrices, one which guarantees a quantifiable transient performance improvement, and the other exponential convergence under conditions that are strictly weaker than regressor persistence of excitation;and (iii) an alternative estimator ensuring parameter estimation in finite-time that retains its alertness to track time-varying parameters. Simulations that illustrate our results are also presented. Copyright © 2019, The Authors. All rights reserved.

关键词： Adaptive control systems

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Results from the robocademy ITN: Autonomy, disturbance rejection and perception for advanced marine robotics

arXiv

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arXiv 2019年

作者： Valdenegro-Toro, Matias de Lucas Alvarez, Mariela Dmitrieva, Mariia Wehbe, Bilal Salavasidis, Georgios Heshmati-Alamdari, Shahab Fuentes-Pérez, Juan F. Yordanova, Veronika Istenič, Klemen Guerneve, Thomas Ocean Systems Lab Heriot-Watt University EdinburghEH14 4AS United Kingdom DFKI - Robotic Innovation Center Bremen Germany Marine Autonomous and Robotics Systems National Oceanography Centre SouthamptonSO14 1NJ United Kingdom Control Systems Lab Department of Mechanical Engineering National Technical University of Athens 9 Heroon Polytechniou Street Zografou15780 Greece Centre for Biorobotics Tallin University of Technology Tallinn Estonia Atlas Elektronik GmbH Sebaldsbruecker Heerstrasse 235 Bremen Germany University of Girona C\Pic de Peguera 13 Girona17003 Spain SeeByte Orchard Brae House 30 Queensferry Rd EdinburghEH4 2HS United Kingdom

Marine and Underwater resources are important part of the economy of many countries. This requires significant financial resources into their construction and maintentance. robotics is expected to fill this void, by automating and/or removing humans from hostile environments in order to easily perform maintenance tasks. The Robocademy Marie SklodowskaCurie Initial Training Network was funded by the European Union's FP7 research program in order to train 13 Fellows into world-leading researchers in Marine and Underwater robotics. The fellows developed guided research into three areas of key importance: Autonomy, Disturbance Rejection, and Perception. This paper presents a summary of the fellows' research in the three action lines. 71 scientific publications were the primary result of this project, with many other publications currently in the pipeline. Most of the fellows have found employment in Europe, which shows the high demand for this kind of experts. We believe the results from this project are already having an impact in the marine robotics industry, as key technologies are being adopted already. Copyright © 2019, The Authors. All rights reserved.

关键词： Disturbance rejection

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Steady‐state security evaluation of electrical power systems by means of artificial neural network

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European Transactions on Electrical Power 1995年第2期5卷 91-97页

作者： Chiodo, E. Menniti, D. Testa, A. Picardi, C. Elio Chiodo (1959) received the degree in Electronics Engineering in 1985 and the Ph.D. degree in Computational Statistics both from the University of Naplefltaly. He is a Researcher at the Department of Electrical Engineering of the University of Naples and a member of the Italian Statistical Society. His areas of interest include probabilistic methods applied to electric power systems analysis. (University of Naples Fedrrico 11. Electrical Engineering Dept.via Claudio 21 1-80125 Naplefltaly T +3981/7683226 Fax+3981/2396897) Daniele Menniti (1958) received the degree in Electrical Engineering from the University of Calabria. Cosenzataly and the Ph.D. degree in Electrical Engineering from the University of NapleslItaly in 1984 and 1989 respectively. He is a researcher at the Electronic. Computer and Systems Science Department of the University of Calabria. Italy. Hiscurrent research interests concern electric power system analysis real-time control and automation. (University of Calabria Electronic Computer and Systems Science Dep. Arcavacataji Rende (CS). 1-87036 CosenzdItaly T +39984/494707. Fax +39984/4947 13) Alfredo Testa (1950) received the degree in Electrical Engineering from the University of Naples/Italy in 1975. He is an Associate Professor in Electrical Power Systems at the Department of Electrical Engineering of the University of Naples. He is engaged in researches on electrical power systems reliability and harmonic analysis. (University of Naples Federico 11. Electrical Engineering Dep. via Claudio '2 1 1-80 I25 NapleslItaly T + 39 8 I/7 68 3'2 11. Fax+3981/2396897) Ciro Picardi (1949) received the degree in Electronics Engineering from the University of Naples/Italy in 1975. He is currently Associate Professor in Process Control at the Department of Electronic Computer and System Science of the University of Calabria. Italy. His current research interests are in the area of electrical drives robotics neural networks and fuzzy control. (University of Calabria Electronic. Compu

An artificial‐neural‐network (ANN) application for steady‐state security evaluation of electrical power systems is presented. Such application is based upon a combined use of a multilayer back‐propagation neural network and of an opportune electrical system model, previously proposed by some of the authors. In the paper, the definition of steady‐state security and the problems arising from its on‐line evaluation are briefly recalled. Then, the ANN utilized is described with particular reference to the solutions adopted by the authors. Afterwards, some remarks on the system model utilized are reported. Finally, numerical applications on the AEP‐14 bus system are presented and the results obtained with different ANN configurations are analyzed. Copyright © 1995 John Wiley & Sons, Ltd.

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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