检索结果-内蒙古大学图书馆

Brain based BMIgap as a new tool to correlate obesity and neural alterations – a multicohort study

学校读者我要写书评

暂无评论

Neuroscience Applied 2022年 1卷

作者： A. Khuntia R. Paul O.A. Andreassen F. Degenhard R. Eils J. Erdmann C. Herrmann M. Hofmann-Apitius T. Kaufmann A.T. Kodamullil S. Mucha M.M. Nöthen M.L. Pedersen A. Quintero H. Schunkert A. Sharma H. Tost L.T. Westlye Y. Zhang J. Kambeitz N. Koutsouleris 1Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University Psychiatry Munich Germany 2Max Plank Institute of Psychiatry Psychiatry Munich Germany 3Department of Psychiatry and Psychotherapy- Ludwig-Maximilian-University- Munich- Germany Department of Psychiatry and Psychotherapy München Germany 4Norwegian Centre for Mental Disorders Research NORMENT Division of Mental Health and Addiction Oslo Norway 5Institute of Human Genetics- University of Bonn School of Medicine Bonn Germany 6Health Data Science Unit Heidelberg University Hospital- and BioQuant Heidelberg Germany 7Institute for Cardiogenetics University of Lübeck Lübeck Germany 8Heidelberg University Hospital- and BioQuant Health Data Science Unit Heidelberg Germany 9Fraunhofer Institute for Algorithms and Scientific Computing SCAI Bioinformatics Sankt Augustin Germany 10Fraunhofer Institute for Algorithms and Scientific Computing SCAI- Bioinformatics Sankt Augustin Germany 11Institute for Cardiogenetics- University of Lübeck- DZHK Cardiogenetics Lübeck Germany 12Institute of Human Genetics- University of Bonn Human Genetics Bonn Germany 13University of Oslo Department of Psychology Oslo Norway 14Deutsches Herzzentrum München- Technische Universität München Department of Cardiology Munich Germany 15Heidelberg University Hospital- and BioQuant Health Data Science Unit- Heidelberg Germany 16Central Institute of Mental Health Department of Psychiatry and Psychotherapy- Mannheim Germany 17University of Cologne Department of Psychiatry and Psychotherapy- Cologne Germany 18University of Turku Department of Psychiatry- Turku Finland 19Neuroscience and Sense Organs- University of Bari Aldo Moro Department of Basic Medical Science Bari Italy 20Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico Department of Neurosciences and Mental Health Milano Italy 21Institute of Mental Health- University of Birmingham Psychiatry Birmingham United Kingdom 22University of Birmingham School of Psyc

Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

学校读者我要写书评

暂无评论

Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

Diabetic foot ulcers segmentation challenge report: Benchmark and analysis

学校读者我要写书评

暂无评论

Medical image analysis 2024年 94卷 103153页

作者： Moi Hoon Yap Bill Cassidy Michal Byra Ting-Yu Liao Huahui Yi Adrian Galdran Yung-Han Chen Raphael Brüngel Sven Koitka Christoph M Friedrich Yu-Wen Lo Ching-Hui Yang Kang Li Qicheng Lao Miguel A González Ballester Gustavo Carneiro Yi-Jen Ju Juinn-Dar Huang Joseph M Pappachan Neil D Reeves Vishnu Chandrabalan Darren Dancey Connah Kendrick Department of Computing and Mathematics Manchester Metropolitan University John Dalton Building Chester Street Manchester M1 5GD United Kingdom Lancashire Teaching Hospitals NHS Trust Preston PR2 9HT United Kingdom. Electronic address: m.yap@mmu.ac.uk. Department of Computing and Mathematics Manchester Metropolitan University John Dalton Building Chester Street Manchester M1 5GD United Kingdom. Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland RIKEN Center for Brain Science Wako Japan. Department of Computer Science National Tsing Hua University No. 101 Section 2 Kuang-Fu Road Hsinchu Taiwan. West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China. BCN Medtech Universitat Pompeu Fabra Barcelona Spain AIML University of Adelaide Australia. Institute of Electronics National Yang Ming Chiao Tung University No. 1001 University Road Hsinchu 300 Taiwan. Department of Computer Science University of Applied Sciences and Arts Dortmund (FH Dortmund) Emil-Figge-Str. 42 44227 Dortmund Germany Institute for Medical Informatics Biometry and Epidemiology (IMIBE) University Hospital Essen Zweigertstr. 37 45130 Essen Germany Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Girardetstr. 2 45131 Essen Germany. Institute for Artificial Intelligence in Medicine (IKIM) University Hospital Essen Girardetstr. 2 45131 Essen Germany Institute of Diagnostic and Interventional Radiology and Neuroradiology University Hospital Essen Hufelandstr. 55 45147 Essen Germany. Institute for Medical Informatics Biometry and Epidemiology (IMIBE) University Hospital Essen Zweigertstr. 37 45130 Essen Germany. West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu China Shanghai Artificial Intelligence Laboratory Shanghai China. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China BCN Medtech Universit

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Metrics Segmentation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liao, Hao Liu, Ming-Kai Mariani, Manuel Sebastian Zhou, Mingyang Wu, Xingtong National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu610051 China URPP Social Networks Universität Zürich CH-8050 Switzerland

When investigating the spreading of a piece of information or the diffusion of an innovation, we often lack information on the underlying propagation network. Reconstructing the hidden propagation paths based on the observed diffusion process is a challenging problem which has recently attracted attention from diverse research fields. To address this reconstruction problem, based on static similarity metrics commonly used in the link prediction literature, we introduce new node-node temporal similarity metrics. The new metrics take as input the time-series of multiple independent spreading processes, based on the hypothesis that two nodes are more likely to be connected if they were often infected at similar points in time. This hypothesis is implemented by introducing a time-lag function which penalizes distant infection times. We find that the choice of this time-lag strongly affects the metrics' reconstruction accuracy, depending on the network's clustering coefficient and we provide an extensive comparative analysis of static and temporal similarity metrics for network reconstruction. Our findings shed new light on the notion of similarity between pairs of nodes in complex networks. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

Corrigendum to "Compounds co-targeting kinases in axon regulatory pathways promote regeneration and behavioral recovery after spinal cord injury in mice" [Exp. Neurol. 355 (2022) 114117]

学校读者我要写书评

暂无评论

Experimental neurology 2024年 373卷 114669页

作者： Kar Men Mah Wei Wu Hassan Al-Ali Yan Sun Qi Han Ying Ding Melissa Muñoz Xiao-Ming Xu Vance P Lemmon John L Bixby The Miami Project to Cure Paralysis Dept of Neurological Surgery University of Miami Miami FL 33136 USA. Department of Neurological Surgery and Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46202 USA. The Miami Project to Cure Paralysis Dept of Neurological Surgery University of Miami Miami FL 33136 USA Peggy and Harold Katz Family Drug Discovery Center Dept of Medicine University of Miami Miami FL 33136 USA Sylvester Comprehensive Cancer Center University of Miami Miami FL 33136 USA. Department of Neurological Surgery and Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46202 USA Department of Anatomy Histology and Embryology School of Basic Medical Sciences Fudan University Shanghai 200032 China. Sylvester Comprehensive Cancer Center University of Miami Miami FL 33136 USA Institute for Data Science and Computing University of Miami Miami FL 33136 USA. Electronic address: vlemmon@miami.edu. Dept of Molecular and Cellular Pharmacology University of Miami Miami FL 33136 USA. Electronic address: jbixby@miami.edu.

A survey on edge computing systems and tools

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Liu, Fang Tang, Guoming Li, Youhuizi Cai, Zhiping Zhang, Xingzhou Zhou, Tongqing School of Data and Computer Science Sun Yat-sen University GuangzhouGuangdong China Key Laboratory of Science and Technology on Information System Engineering National University of Defense Technology ChangshaHunan China School of Compute Science and Technology Hangzhou Dianzi University China College of Computer National University of Defense Technology ChangshaHunan China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China

—Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

iSplit LBI: Individualized partial ranking with ties via split LBI

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

Synergy of Binary Substitutions for Improving the Cycle Performance in LiNiO2 Revealed by Ab Initio Materials Informatics

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Yoshida, Tomohiro Maezono, Ryo Hongo, Kenta Department of Computer-Aided Engineering and Development Sumitomo Metal Mining Co. Ltd. 3-5 Sobiraki-cho NiihamaEhime792-0001 Japan School of Information Science JAIST Asahidai 1-1 IshikawaNomi923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa SaitamaWako351-0198 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 NomiIshikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen TsukubaIbaraki305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho SaitamaKawaguchi-shi322-0012 Japan

We explore LiNiO2-based cathode materials with two-element substitutions by an ab initio simulation based materials informatics (AIMI) approach. According to our previous study, a higher cycle performance strongly correlates with less structural change during charge–discharge cycles;the latter can be used for evaluating the former. However, if we target the full substitution space, full simulations are infeasible even for all binary combinations. To circumvent such an exhaustive search, we rely on Bayesian optimization. Actually, by searching only 4% of all the combinations, our AIMI approach discovered two promising combinations, Cr-Mg and Cr-Re, whereas each atom itself never improved the performance. We conclude that the synergy never emerges from a common strategy restricted to combinations of "good" elements that individually improve the performance. In addition, we propose a guideline for the binary substitutions by elucidating the mechanism of crystal structure change. Copyright © 2020, The Authors. All rights reserved.

关键词： Binary alloys

Biomedical image analysis competitions: The state of current participation practice

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking