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Stability evaluation method for special purpose ACS based on sensitivity theory

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AIP Conference Proceedings 2022年第1期2383卷

作者： Filatov Vladimir Ivanovich Fedorova Veronika Anatolievna Chipchagov Mikhail Sergeevich Zaitsev Mikhail Alekseevich 1Department “Computer security” of faculty “Informatics and control systems” of Bauman Moscow State Technical University 5/1 2ya Baumanskaya st. Moscow 105005 Russia 2Faculty “Big Data Analysis and Machine Learning in Economics and Finance” of Financial University under the Government of the Russian Federation Moscow Russia 3Faculty “Informational system” of Moscow Witte University Moscow Russia

The article deals with the use of complex special purpose automated control systems (ACS), which are characterized by a significant complication of the control process by nodes or elements included in the control loop...

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Tumor Detection, Segmentation and Classification Challenge on Automated 3D Breast Ultrasound: The TDSC-ABUS Challenge

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arXiv 2025年

作者： Luo, Gongning Xu, Mingwang Chen, Hongyu Liang, Xinjie Tao, Xing Ni, Dong Jeong, Hyunsu Kim, Chulhong Stock, Raphael Baumgartner, Michael Kirchhoff, Yannick Rokuss, Maximilian Maier-Hein, Klaus Yang, Zhikai Fan, Tianyu Boutry, Nicolas Tereshchenko, Dmitry Moine, Arthur Charmetant, Maximilien Sauer, Jan Du, Hao Bai, Xiang-Hui Raikar, Vipul Pai Montoya-Del-Angel, Ricardo Martí, Robert Luna, Miguel Lee, Dongmin Qayyum, Abdul Mazher, Moona Guo, Qihui Wang, Changyan Awasthi, Navchetan Zhao, Qiaochu Wang, Wei Wang, Kuanquan Wang, Qiucheng Dong, Suyu School of Computer Science and Technology Harbin Institute of Technology Harbin150001 China Department of Mathematics Faculty of Science National University of Singapore Singapore National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Laboratory Shenzhen University Shenzhen China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Pohang Korea Republic of Heidelberg Division of Medical Image Computing Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany Department of Biomedical Engineering and Health KTH Royal Institute of Technology Stockholm Sweden France FathomX Singapore Saw Swee Hock School of Public Health National University of Singapore Singapore Philips Research University of Girona Spain Department of Robotics and Mechatronics Engineering DGIST Korea Republic of Department of Interdisciplinary Studies of Artificial Intelligence DGIST Korea Republic of National Heart and Lung Institute Faculty of Medicine Imperial College London London United Kingdom Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom Lab School of Communication and Information Engineering Shanghai University Shanghai China Faculty of Science Mathematics and Computer Science Informatics Institute University of Amsterdam Amsterdam1090 GH Netherlands Department of Biomedical Engineering and Physics Amsterdam UMC Amsterdam1081 HV Netherlands Xi’an Jiaotong-Liverpool University China Department of Ultrasound Harbin Medical University Cancer Hospital No. 150 Haping Road Nangang

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps in reducing the number of deaths. Automated 3D Breast Ultrasound (ABUS) is a newer approach for breast screening, which has many advantages over handheld mammography such as safety, speed, and higher detection rate of breast cancer. Tumor detection, segmentation, and classification are key components in the analysis of medical images, especially challenging in the context of 3D ABUS due to the significant variability in tumor size and shape, unclear tumor boundaries, and a low signal-to-noise ratio. The lack of publicly accessible, well-labeled ABUS datasets further hinders the advancement of systems for breast tumor analysis. Addressing this gap, we have organized the inaugural Tumor Detection, Segmentation, and Classification Challenge on Automated 3D Breast Ultrasound 2023 (TDSC-ABUS2023). This initiative aims to spearhead research in this field and create a definitive benchmark for tasks associated with 3D ABUS image analysis. In this paper, we summarize the top-performing algorithms from the challenge and provide critical analysis for ABUS image examination. We offer the TDSC-ABUS challenge as an open-access platform at https://***/ to benchmark and inspire future developments in1algorithmic research. © 2025, CC BY.

关键词： Image segmentation

A split-and-perturb decomposition of number-conserving cellular automata

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arXiv 2019年

作者： Wolnik, Barbara Nenca, Anna Baetens, Jan M. de Baets, Bernard Institute of Mathematics Faculty of Mathematics Physics and Informatics University of Gdá nsk Gdá nsk80-308 Poland KERMIT Department of Data Analysis and Mathematical Modelling Faculty of Bioscience Engineering Ghent University Coupure links 653 GentB-9000 Belgium

This paper concerns d-dimensional cellular automata with the von Neumann neighborhood that conserve the sum of the states of all their cells. These automata, called number-conserving or density-conserving cellular automata, are of particular interest to mathematicians, computer scientists and physicists, as they can serve as models of physical phenomena obeying some conservation law. We propose a new approach to study such cellular automata that works in any dimension d and for any set of states Q. Essentially, the local rule of a cellular automaton is decomposed into two parts: a split function and a perturbation. This decomposition is unique and, moreover, the set of all possible split functions has a very simple structure, while the set of all perturbations forms a linear space and is therefore very easy to describe in terms of its basis. We show how this approach allows to find all number-conserving cellular automata in many cases of d and Q. In particular, we find all three-dimensional number-conserving CAs with three states, which until now was beyond the capabilities of computers. Copyright © 2019, The Authors. All rights reserved.

关键词： Cellular automata

A systematic review of challenges and proposed solutions in modeling multimodal data

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arXiv 2025年

作者： Farhadizadeh, Maryam Weymann, Maria Blaß, Michael Kraus, Johann Gundler, Christopher Walter, Sebastian Hempen, Noah Binder, Harald Binder, Nadine Institute of General Practice/Family Medicine Faculty of Medicine Medical Center University of Freiburg Elsässer Straße 2M Freiburg79110 Germany Freiburg Center for Data Analysis Modeling and AI University of Freiburg Ernst-Zermelo-Str. 1 Freiburg79104 Germany Institute of Medical Biometry and Statistics Faculty of Medicine Medical Center University of Freiburg Stefan-Meier-Straße 26 Freiburg79104 Germany Institute for Applied Medical Informatics University Medical Center Hamburg-Eppendorf Martinistraße 52 Hamburg20251 Germany Institute of Medical Systems Biology Ulm University Helmholtzstraße 16 Ulm89081 Germany Department of Computer Science Faculty of Engineering University of Freiburg Georges-Köhler-Allee 051 Freiburg79110 Germany

Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications. © 2025, CC BY.

关键词： Clinical research

The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

Complementary time-frequency domain networks for dynamic parallel MR image reconstruction

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arXiv 2020年

作者： Qin, Chen Duan, Jinming Hammernik, Kerstin Schlemper, Jo Küstner, Thomas Botnar, René Prieto, Claudia Price, Anthony N. Hajnal, Joseph V. Rueckert, Daniel Institute for Digital Communications School of Engineering University of Edinburgh Edinburgh United Kingdom Department of Computing Imperial College London London United Kingdom School of Computer Science University of Birmingham Birmingham United Kingdom Institute for AI and Informatics Klinikum Rechts der Isar Technical University of Munich Munich Germany Hyperfine Research Inc. GuilfordCT United States School of Biomedical Engineering and Imaging Sciences King's College London St. Thomas' Hospital London United Kingdom Department of Diagnostic and Interventional Radiology Medical Image and Data Analysis University Hospital of Tuebingen Tuebingen Germany

Purpose To introduce a novel deep learning based approach for fast and high-quality dynamic multi-coil MR reconstruction by learning a complementary time-frequency domain network that exploits spatio-temporal correlations simultaneously from complementary domains. Theory and Methods Dynamic parallel MR image reconstruction is formulated as a multi-variable minimisation problem, where the data is regularised in combined temporal Fourier and spatial (x-f ) domain as well as in spatio-temporal image (x-t) domain. An iterative algorithm based on variable splitting technique is derived, which alternates among signal de-aliasing steps in x-f and x-t spaces, a closed-form point-wise data consistency step and a weighted coupling step. The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains. Results Experiments were performed on two datasets of highly undersampled multi-coil short-axis cardiac cine MRI scans. Results demonstrate that our proposed method outperforms the current state-of-the-art approaches both quantitatively and qualitatively. The proposed model can also generalise well to data acquired from a different scanner and data with pathologies that were not seen in the training set. Conclusion The work shows the benefit of reconstructing dynamic parallel MRI in complementary time-frequency domains with deep neural networks. The method can effectively and robustly reconstruct high-quality images from highly undersampled dynamic multi-coil data (16× and 24× yielding 15s and 10s scan times respectively) with fast reconstruction speed (2.8s). This could potentially facilitate achieving fast single-breath-hold clinical 2D cardiac cine imaging. © 2020, CC BY.

关键词： Magnetic resonance imaging

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge

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arXiv 2020年

作者： Zhuang, Xiahai Xu, Jiahang Luo, Xinzhe Chen, Chen Ouyang, Cheng Rueckert, Daniel Campello, Victor M. Lekadir, Karim Vesal, Sulaiman RaviKumar, Nishant Liu, Yashu Luo, Gongning Chen, Jingkun Li, Hongwei Ly, Buntheng Sermesant, Maxime Roth, Holger Zhu, Wentao Wang, Jiexiang Ding, Xinghao Wang, Xinyue Yang, Sen Li, Lei School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Biomedical Image Analysis Group Imperial College London London United Kingdom Department Mathematics & Computer Science Universitat de Barcelona Barcelona Spain Friedrich-Alexander-Universität Erlangen-Nürnberg Germany School of Computer Science and Technology Harbin Institute of Technology Harbin China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Department of Informatics Technical University of Munich Germany INRIA Université Côte d’Azur Sophia Antipolis France NVIDIA Bethesda United States School of Informatics Xiamen University Xiamen China College of Electrical Engineering Sichuan University Chengdu China Tencent AI Lab Shenzhen China

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, automated segmentation of LGE CMR is still challenging, due to the indistinguishable boundaries, heterogeneous intensity distribution and complex enhancement patterns of pathological myocardium from LGE CMR. Furthermore, compared with the other sequences LGE CMR images with gold standard labels are particularly limited, which represents another obstacle for developing novel algorithms for automatic segmentation of LGE CMR. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation and compare them objectively. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and unsupervised methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challeng

关键词： Magnetic resonance

Epidemiological and public health requirements for COVID-19 contact tracing apps and their evaluation

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arXiv 2021年

作者： Colizza, Vittoria Grill, Eva Mikolajczyk, Rafael Cattuto, Ciro Kucharski, Adam Riley, Steven Kendall, Michelle Lythgoe, Katrina Abeler-Dörner, Lucie Wymant, Chris Bonsall, David Ferretti, Luca Fraser, Christophe INSERM Sorbonne Université Institut Pierre Louis d’Epidémiologie et de Santé Publique IPLESP Paris France Tokyo Tech World Research Hub Initiative Institute of Innovative Research Tokyo Institute of Technology Tokyo Japan Institute für Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians University Muenchen Muenchen Germany Institute for Medical Epidemiology Biometrics and Informatics Interdisciplinary Center for Health Sciences Martin Luther University Halle-Wittenberg Halle Germany University of Turin Turin Italy ISI Foundation Turin Italy Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London United Kingdom Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Medicine University of Oxford United Kingdom Department of Statistics University of Warwick Warwick United Kingdom

Digital contact tracing is a public health intervention. It should be integrated with local health policy, provide rapid and accurate notifications to exposed individuals, and encourage high app uptake and adherence to quarantine. Real-time monitoring and evaluation of effectiveness of app-based contact tracing is key for improvement and public trust. © 2021, CC BY-NC-SA.

关键词： Public health

Correction: APOE and TREM2 regulate amyloid-responsive microglia in Alzheimer's disease

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Acta neuropathologica 2023年第4期146卷 661页

作者： Aivi T Nguyen Kui Wang Gang Hu Xuran Wang Zhen Miao Joshua A Azevedo EunRan Suh Vivianna M Van Deerlin David Choi Kathryn Roeder Mingyao Li Edward B Lee Translational Neuropathology Research Laboratory Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania 613A Stellar Chance Laboratories 422 Curie Blvd Philadelphia PA 19104 USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania 213 Blockley Hall 423 Guardian Drive Philadelphia PA 19104 USA. Department of Information Theory and Data Science School of Mathematical Sciences and LPMC Nankai University Tianjin China. School of Statistics and Data Science Key Laboratory for Medical Data Analysis and Statistical Research of Tianjin Nankai University Tianjin China. Department of Statistics and Data Science Carnegie Mellon University Pittsburgh PA USA. Graduate Group in Genomics and Computational Biology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Heinz College of Public Policy and Information Systems Carnegie Mellon University Pittsburgh PA USA. Computational Biology Department Carnegie Mellon University Pittsburgh PA USA. Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania 213 Blockley Hall 423 Guardian Drive Philadelphia PA 19104 USA. mingyao@pennmedicine.upenn.edu. Translational Neuropathology Research Laboratory Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania 613A Stellar Chance Laboratories 422 Curie Blvd Philadelphia PA 19104 USA. edward.lee@pennmedicine.upenn.edu. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. edward.lee@pennmedicine.upenn.edu.