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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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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

Trapped mode control in metasurfaces composed of particles with the form birefringence property

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arXiv 2022年

作者： Kupriianov, Anton S. Fesenko, Volodymyr I. Evlyukhin, Andrey B. Han, Wei Tuz, Vladimir R. College of Physics International Center of Future Science Jilin University 2699 Qianjin St. Changchun130012 China Institute of Radio Astronomy of National Academy of Sciences of Ukraine 4 Mystetstv St. Kharkiv61002 Ukraine Institute of Quantum Optics Leibniz Universität Hannover Hannover30167 Germany State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University 2699 Qianjin Street Changchun130012 China School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University 4 Svobody Square Kharkiv61022 Ukraine

Progress in developing advanced photonic devices relies on introducing new materials, discovered physical principles, and optimal designs when constructing their components. Optical systems operating on the principles of excitation of extremely high-quality factor trapped modes (also known as the bound states in the continuum, BICs) are of great interest since they allow the implementation of laser and sensor devices with outstanding characteristics. In this paper, we discuss how one can utilize the anisotropic properties of novel materials (transition metal dichalcogenides, TMDs), particularly, the bulk molybdenum disulfide (MoS2), to realize the excitation of trapped modes in dielectric metasurfaces. The bulk MoS2 is a thin-film structure in which the light wave behaves the same way as that in the uniaxial anisotropic material with the form birefringence property. Our metasurface is composed of an array of disk-shaped nanoparticles (resonators) made of the MoS2 material under the assumption that the anisotropy axis of MoS2 can be tilted to the rotation axis of the disks. We perform a detailed analysis of eigenwaves and scattering properties of such anisotropic resonators as well as the spectral features of the metasurface revealing dependence of the excitation conditions of the trapped mode on the anisotropy axis orientation of the MoS2 material used. © 2022, CC BY.

关键词： Transition metals

Explanation Chains Model Based on the Fine-Grained Data 8th

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Explanation Chains Model Based on the Fine-Grained Data

8th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2019

作者： Ma, Fu-Yuan Chen, Wen-Qi Xiao, Min-Hao Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China Changchun Institute of Technology Changchun130012 China College of Software Jilin University Changchun130012 China

ISBN: (纸本)9783030322359

With the development of information society, Recommendation System has been an import tool to help users filter information and create more economic value for enterprises. However, it is difficult for traditional recommendation systems to interpret recommendation results. In order to improve users’ trust in recommendation results, interpretable recommendation models have attracted more and more attention. In this paper, we present the Explanation Chains Model based on the Fine-grained Data (F-ECM) to enhance the effectiveness of recommendation while achieving the interpretability of recommendation. First, we generate parsing trees from user comments and extract three key sentence structure information (i.e., aspects, features and sentiment tendency) from those generated parsing tree. The fine-grained similarity is computed based on the aspects and features of the products to be recommended, and users’ product satisfaction is predicted by combining sentiment tendency. Then the recommendation chain will be constructed according to the satisfaction degree in the recommendation list. Finally, we calculate recommendation chain scores of all the items to be recommended to the target user, generate the recommendation results and personal explanation for the user by the recommendation chains. Experiments in the Amazon data set show that the Explanation Chains Model based on the Fine-grained Data achieve better interpretability and performance of product recommendation systems. © 2019, Springer Nature Switzerland AG.

关键词： Recommender systems

Trapped-mode excitation in all-dielectric metamaterials with loss and gain

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arXiv 2022年

作者： Hlushchenko, Anton V. Novitsky, Denis V. Tuz, Vladimir R. State Key Laboratory of Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University 2699 Qianjin Street Changchun130012 China National Science Center Kharkiv Institute of Physics and Technology National Academy of Sciences of Ukraine 1 Akademicheskaya Street Kharkiv61108 Ukraine School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University 4 Svobody Square Kharkiv61022 Ukraine

Non-Hermitian photonics based on combining loss and gain media within a single optical system provides a number of approaches to control and generate the flow of light. In this paper, we show that by introducing non-Hermitian perturbation into the system with loss and gain constituents, the high-quality resonances known as trapped modes can be excited without the need to change the symmetry of the unit cell geometry. To demonstrate this idea, we consider a widely used all-dielectric planar metamaterial whose unit cell consists of a pair of rectangular nanoantennas made of ordinal (with loss) and doped (with gain) silicon. Since the quality factor of the trapped-mode resonance can be controlled by changing both spatial symmetry and non-Hermiticity, varying loss and gain allows us to compensate for the influence of asymmetry and restore the quality factor of the localized mode. The results obtained suggest new ways to achieve high-quality resonances in non-Hermitian metamaterials promising for many practical applications in nanophotonics. © 2022, CC BY.

关键词： Metamaterials

VDPC: Variational Density Peak Clustering Algorithm

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TechRxiv

TechRxiv 2021年

作者： Wang, Yizhang Wang, Di Zhou, You Zhang, Xiaofeng Quek, Chai College of Information Engineering Yangzhou University Yangzhou China Institute of Scientific and Technical Information of China Beijing China Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore Joint NTU-WeBank Research Centre on Fintech Nanyang Technological University Singapore College of Computer Science and Technology Jilin University Changchun China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore

The widely applied density peak clustering (DPC) algorithm makes an intuitive cluster formation assumption that cluster centers are often surrounded by data points with lower local density and far away from other data points with higher local density. However, this assumption suffers from one limitation that it is often problematic when identifying clusters with lower density because they might be easily merged into other clusters with higher density. As a result, DPC may not be able to identify clusters with variational density. To address this issue, we propose a variational density peak clustering (VDPC) algorithm, which is designed to systematically and autonomously perform the clustering task on datasets with various types of density distributions. Specifically, we first propose a novel method to identify the representatives among all data points and construct initial clusters based on the identified representatives for further analysis of the clusters’ property. Furthermore, we divide all data points into different levels according to their local density and propose a unified clustering framework by combining the advantages of both DPC and DBSCAN. Thus, all the identified initial clusters spreading across different density levels are systematically processed to form the final clusters. To evaluate the effectiveness of the proposed VDPC algorithm, we conduct extensive experiments using 20 datasets including eight synthetic, six real-world and six image datasets. The experimental results show that VDPC outperforms two classical algorithms (i.e., DPC and DBSCAN) and four state-of-the-art extended DPC algorithms. © 2021, CC BY-NC-SA.

关键词： Clustering algorithms

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China 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 Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

Machine Learning Approach to Enhance the Performance of MNP?Labeled Lateral Flow Immunoassay

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Nano-Micro Letters 2019年第1期11卷 132-146页

作者： Wenqiang Yan Kan Wang Hao Xu Xuyang Huo Qinghui Jin Daxiang Cui Department of Instrument Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Engineering Research Center for Intelligent Diagnosis and Treatment InstrumentKey Laboratory of Thin Film and Microfabrication (Ministry of Education) Shanghai Jiao Tong University School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Department of Biomedical Engineering JiLin Medical University State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo University

The use of magnetic nanoparticle(MNP)-labeled immunochromatography test strips(ICTSs) is very important for point-ofcare testing(POCT). However, common diagnostic methods cannot accurately analyze the weak magnetic signal from ICTSs, limiting the applications of POCT. In this study, an ultrasensitive multiplex biosensor was designed to overcome the limitations of capturing and normalization of the weak magnetic signal from MNPs on ICTSs. A machine learning model for sandwich assays was constructed and used to classify weakly positive and negative samples, which significantly enhanced the specificity and sensitivity. The potential clinical application was evaluated by detecting 50 human chorionic gonadotropin(HCG) samples and 59 myocardial infarction serum samples. The quantitative range for HCG was 1–1000 mIU mL^(-1) and the ideal detection limit was 0.014 mIU mL^(-1), which was well below the clinical threshold. Quantitative detection results of multiplex cardiac markers showed good linear correlations with standard values. The proposed multiplex assay can be readily adapted for identifying other biomolecules and also be used in other applications such as environmental monitoring, food analysis, and national security.

关键词： Point-of-care testing Immunochromatography test strips Magnetic nanoparticles Machine learning Support vector machine

Prediction of energy resolution in the JUNO experiment

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Chinese Physics C 2025年第1期49卷 35-59页

作者： Angel Abusleme Thomas Adam Kai Adamowicz Shakeel Ahmad Rizwan Ahmed Sebastiano Aiello Fengpeng An Qi An Giuseppe Andronico Nikolay Anfimov Vito Antonelli Tatiana Antoshkina Joao Pedro Athayde Marcondes de André Didier Auguste Weidong Bai Nikita Balashov Wander Baldini Andrea Barresi Davide Basilico Eric Baussan Marco Bellato Marco Beretta Antonio Bergnoli Daniel Bick Lukas Bieger Svetlana Biktemerova Thilo Birkenfeld Iwan Blake David Blum Simon Blyth Anastasia Bolshakova Mathieu Bongrand Clément Bordereau Dominique Breton Augusto Brigatti Riccardo Brugnera Riccardo Bruno Antonio Budano Jose Busto Anatael Cabrera Barbara Caccianiga Hao Cai Xiao Cai Yanke Cai Zhiyan Cai Stéphane Callier Steven Calvez Antonio Cammi Agustin Campeny Chuanya Cao Guofu Cao Jun Cao Rossella Caruso Cédric Cerna Vanessa Cerrone Jinfan Chang Yun Chang Auttakit Chatrabhuti Chao Chen Guoming Chen Pingping Chen Shaomin Chen Xin Chen Yiming Chen Yixue Chen Yu Chen Zelin Chen Zhangming Chen Zhiyuan Chen Zikang Chen Jie Cheng Yaping Cheng YuChin Cheng Alexander Chepurnov Alexey Chetverikov Davide Chiesa Pietro Chimenti Yen-Ting Chin Po-Lin Chou Ziliang Chu Artem Chukanov Gérard Claverie Catia Clementi Barbara Clerbaux Marta Colomer Molla Selma Conforti Di Lorenzo Alberto Coppi Daniele Corti Simon Csakli Chenyang Cui Flavio Dal Corso Olivia Dalager Jaydeep Datta Christophe De La Taille Zhi Deng Ziyan Deng Xiaoyu Ding Xuefeng Ding Yayun Ding Bayu Dirgantara Carsten Dittrich Sergey Dmitrievsky Tadeas Dohnal Dmitry Dolzhikov Georgy Donchenko Jianmeng Dong Evgeny Doroshkevich Wei Dou Marcos Dracos Frédéric Druillole Ran Du Shuxian Du Katherine Dugas Stefano Dusini Hongyue Duyang Jessica Eck Timo Enqvist Andrea Fabbri Ulrike Fahrendholz Lei Fan Jian Fang Wenxing Fang Dmitry Fedoseev Li-Cheng Feng Qichun Feng Federico Ferraro Amélie Fournier Fritsch Fritsch Haonan Gan Feng Gao Feng Gao Alberto Garfagnini Arsenii Gavrikov Marco Giammarchi Nunzio Giudice Maxim Gonchar Guanghua Gong Hui Gong Yuri Gornushkin Marco Grassi Maxim Gromov Vasily Gromov Minghao G Yerevan Physics Institute YerevanArmenia Université Libre de Bruxelles BrusselsBelgium Universidade Estadual de Londrina LondrinaBrazil Pontificia Universidade Catolica do Rio de Janeiro Rio de JaneiroBrazil Millennium Institute for SubAtomic Physics at the High-energy Frontier(SAPHIR) ANIDChile Pontificia Universidad Católica de Chile SantiagoChile Beijing Institute of Spacecraft Environment Engineering BeijingChina Beijing Normal University BeijingChina China Institute of Atomic Energy BeijingChina Institute of High Energy Physics BeijingChina North China Electric Power University BeijingChina School of Physics Peking UniversityBeijingChina Tsinghua University BeijingChina University of Chinese Academy of Sciences BeijingChina Jilin University ChangchunChina College of Electronic Science and Engineering National University of Defense TechnologyChangshaChina Chongqing University ChongqingChina Dongguan University of Technology DongguanChina Jinan University GuangzhouChina Sun Yat-sen University GuangzhouChina Harbin Institute of Technology HarbinChina University of Science and Technology of China HefeiChina The Radiochemistry and Nuclear Chemistry Group in University of South China HengyangChina Wuyi University JiangmenChina Shandong University JinanChinaand Key Laboratory of Particle Physics and Particle Irradiation of Ministry of EducationShandong UniversityQingdaoChina Nanjing University NanjingChina Guangxi University NanningChina East China University of Science and Technology ShanghaiChina School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghaiChina Tsung-Dao Lee Institute Shanghai Jiao Tong UniversityShanghaiChina Institute of Hydrogeology and Environmental Geology Chinese Academy of Geological SciencesShijiazhuangChina Nankai University TianjinChina Wuhan University WuhanChina Xi'an Jiaotong University Xi'anChina Xiamen University XiamenChina School of Physics and Microelectronics Zhengzhou UniversityZhengzhouChina Institute of Physics National

This paper presents an energy resolution study of the JUNO experiment,incorporating the latest knowledge acquired during the detector construction *** determination of neutrino mass ordering in JUNO requires an exceptional energy resolution better than 3% at 1 *** achieve this ambitious goal,significant efforts have been undertaken in the design and production of the key components of the JUNO *** factors affecting the detection of inverse beta decay signals have an impact on the energy resolution,extending beyond the statistical fluctuations of the detected number of photons,such as the properties of the liquid scintillator,performance of photomultiplier tubes,and the energy reconstruction *** account for these effects,a full JUNO simulation and reconstruction approach is *** enables the modeling of all relevant effects and the evaluation of associated inputs to accurately estimate the energy *** results of this study reveal an energy resolution of 2.95% at 1 ***,this study assesses the contribution of major effects to the overall energy resolution *** analysis serves as a reference for interpreting future measurements of energy resolution during JUNO data ***,it provides a guideline for comprehending the energy resolution characteristics of liquid scintillator-based detectors.

关键词： energy resolution liquid scintillator detector JUNO simulation reconstruction

Characterization of OAM Antenna Operating in a Multiplexing State

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Characterization of OAM Antenna Operating in a Multiplexing ...

IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Zuxian He Vladimir R. Tuz Patrizia Savi Dmytro Vavriv Volodymyr Fesenko State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University Changchun China School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University Kharkiv Ukraine Electronics and Telecommunications Department (DET) Politecnico di Torino Torino Italy Department of Microwave Electronics Institute of Radio Astronomy National Academy of Sciences of Ukraine Kharkiv Ukraine

ISBN: (纸本)9781665490382

Orbital angular momentum (OAM), which characterizes the helical phase pattern of optical beams, has recently attracted enormous interest due to its potential applications in different practice areas. Here we propose an antenna design for generating multiple beams carrying OAM with different topological states at the same frequency. The numerical full-wave simulation is performed for an actual size OAM generator using the ANSYS HFSS electromagnetic solver, and an antenna prototype operating in the microwave band is fabricated and tested. Obtained results prove that the proposed antenna can be used as a compact and low-cost generator of multiple beams with different OAM states.

关键词： Microwave antennas Antenna measurements Multiplexing Microwave measurement Prototypes Generators Orbits