检索结果-内蒙古大学图书馆

第37届中国控制会议

作者： Haibo Gu Peng Liu Xiong Wang Jinhu L LSC Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Automation University of Science and Technology of China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University

Synchronization of directed complex networks has been extensively studied in the previous two decades due to its potential applications in the real world practices. To reveal and analyze the inherent mechanism of global synchronization in the complex networks with directed topologies, this paper attempts to developing a novel synchronization approach by using algebraic connectivity and PI control protocols. First, we studied the distributed PI control for synchronization in directed strongly connected networks and obtained some sufficient conditions to guarantee the networks reach global ***, a numerical example is given to verify the effectiveness of the proposed theoretical results.

关键词： Synchronization distributed PI control strongly connected network network control systems

来源：评论

学校读者我要写书评

暂无评论

Improving Breast Cancer Diagnosis in Ultrasound Images Using Deep Learning with Feature Fusion and Attention Mechanism

引用

Academic radiology 2025年

作者： Sohaib Asif Yuqi Yan Bojian Feng Meiling Wang Yuxin Zheng Tian Jiang Ruyi Fu Jincao Yao Lujiao Lv Mei Song Lin Sui Zheng Yin Vicky Yang Wang Dong Xu Taizhou Key Laboratory of Minimally Invasive Interventional Therapy & Artificial Intelligence Taizhou Campus of Zhejiang Cancer Hospital (Taizhou Cancer Hospital) Taizhou Zhejiang 317502 China (S.A. Y.Y. B.F. L.S. D.X.) Center of Intelligent Diagnosis and Therapy (Taizhou) Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Taizhou Zhejiang 317502 China (S.A. Y.Y. B.F. L.S. V.Y.W. D.X.) Wenling Institute of Big Data and Artificial Intelligence in Medicine Taizhou Zhejiang 317502 China (S.A. Y.Y. B.F. L.S. V.Y.W. D.X.) Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou Zhejiang 310022 China (S.A. Y.Y. B.F. T.J. J.Y. L.L. M.S. M.S. L.S. V.Y.W. D.X.). Department of Diagnostic Ultrasound Imaging & Interventional Therapy Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences Hangzhou Zhejiang 310022 China (S.A. Y.Y. B.F. T.J. J.Y. L.L. M.S. M.S. L.S. V.Y.W. D.X.) Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China (Y.Y. T.J. J.Y. L.S. D.X.). Second Clinical College Zhejiang University of Traditional Chinese Medicine Hangzhou China (M.W. Y.Z. Z.Y.). Hangzhou Fuyang Hospital of Orthopedics of Traditional Chinese Medicine Special Inspection Department Zhejiang China (R.F.). Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China (Y.Y. T.J. J.Y. L.S. D.X.) Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province Hangzhou 310022 China (J.Y.). Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology Hangzhou 310022 China (Y.Y. T.J. J.Y. L.S. D.X.). Electronic address: xudong@***.

RATIONALE AND OBJECTIVES:Early detection of malignant lesions in ultrasound images is crucial for effective cancer diagnosis and treatment. While traditional methods rely on radiologists, deep learning models can improve accuracy, reduce errors, and enhance efficiency. This study explores the application of a deep learning model for classifying benign and malignant lesions, focusing on its performance and interpretability. MATERIALS AND METHODS:In this study, we proposed a feature fusion-based deep learning model for classifying benign and malignant lesions in ultrasound images. The model leverages advanced architectures such as MobileNetV2 and DenseNet121, enhanced with feature fusion and attention mechanisms to boost classification accuracy. The clinical dataset comprises 2171 images collected from 1758 patients between December 2020 and May 2024. Additionally, we utilized the publicly available BUSI dataset, consisting of 780 images from female patients aged 25 to 75, collected in 2018. To enhance interpretability, we applied Grad-CAM, Saliency Maps, and shapley additive explanations (SHAP) techniques to explain the model's decision-making. A comparative analysis with radiologists of varying expertise levels is also conducted. RESULTS:The proposed model exhibited the highest performance, achieving an AUC of 0.9320 on our private dataset and an area under the curve (AUC) of 0.9834 on the public dataset, significantly outperforming traditional deep convolutional neural network models. It also exceeded the diagnostic performance of radiologists, showcasing its potential as a reliable tool for medical image classification. The model's success can be attributed to its incorporation of advanced architectures, feature fusion, and attention mechanisms. The model's decision-making process was further clarified using interpretability techniques like Grad-CAM, Saliency Maps, and SHAP, offering insights into its ability to focus on relevant image features for accurate classi

关键词： Attention mechanisms Deep learning Feature fusion Medical diagnostics Ultrasound imaging

来源：评论

学校读者我要写书评

暂无评论

Global Pinning Synchronization with PI Controller in General Complex Directed Networks

Global Pinning Synchronization with PI Controller in General...

引用

第37届中国控制会议

作者： Peng Liu Haibo Gu Jinhu Lü Yu Kang Department of Automation University of Science and Technology of China LSC Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environmentand Beijing Advanced Innovation Center for Big Data and Brain Machine IntelligenceBeihang University

Global pinning synchronization in complex networks has been extensively studied in the last decades due to its potential *** question is how to design an available controller to achieve global synchronization when the network is *** we all know,the classical PI controller is most widely and successfully used in engineering systems with nonlinear ***,the global synchronization with PI controller in general complex directed networks still remain *** this paper we will theoretically show that the global synchronization of some directed networks can also be ensured by designing an appropriate PI controller and pioneering some selected nodes,then a simple numerical example will also be given to verify our main results.

关键词： Directed network Pining synchronization PI controller Lyapunov method

来源：评论

学校读者我要写书评

暂无评论

Acoustic and Kinematic Examination of Dysarthria in Cantonese Patients of Parkinson’s Disease

Acoustic and Kinematic Examination of Dysarthria in Cantones...

引用

International Symposium on Chinese Spoken Language Processing

作者： Yue Sun Manwa L. Ng Chongyuan Lian Lan Wang Feng Yang Nan Yan College of Big Data and Information Engineering Guizhou University Guiyang China Speech Science Laboratory University of Hong Kong Hong Kong China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Speech Therapy Shenzhen Children’s Hospital Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Instimtes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (纸本)9781538656280;9781538656273

Hypokinetic dysarthria is one of the core symptoms of Parkinson's disease, characterized by reduced loudness, slurred speech and distorted consonant productions. Dopaminergic medication for Parkinson's disease (PD) has been proven to be effective in treating limb and gross motor movement problems. However, the literature sees contradictory findings regarding dopaminergic effects on speech problems associated with PD. Previous perceptual and acoustic studies of PD hypokinetic dysarthria mostly involved heterogeneous population and variations of speech tasks which complicated data interpretation. Also, the lack of kinematic data limited our understanding of the details of articulation errors associated with PD, as well as the medication effects. Electromagnetic articulography (EMA) enabled the examination of PD articulatory patterns with high degree of accuracy and safety. The aim of the present study was to address these inconsistencies by providing an integrative description of basic kinematic and acoustic parameters of speech production about the dopaminergic effect on early PD speech by using EMA. The results revealed a significant improvement of articulatory function on dopaminergic effects for PD speech, evidenced by increased vowel space, the velocity and accelerated velocity initiation and coordination of articulation during bilabial or alveolar productions.

关键词： Kinematics Acoustics Auditory system Sun Parameter extraction Schedules Lips

来源：评论

学校读者我要写书评

暂无评论

Cross-cultural validation and measurement invariance of anxiety and depression symptoms: A study of the Brief Symptom Inventory (BSI) in 42 countries

引用

Journal of affective disorders 2024年 350卷 991-1006页

作者： Gonzalo R Quintana Fernando P Ponce Javier I Escudero-Pastén Juan F Santibáñez-Palma Léna Nagy Mónika Koós Shane W Kraus Zsolt Demetrovics Marc N Potenza Rafael Ballester-Arnal Dominik Batthyány Sophie Bergeron Joël Billieux Peer Briken Julius Burkauskas Georgina Cárdenas-López Joana Carvalho Jesús Castro-Calvo Lijun Chen Giacomo Ciocca Ornella Corazza Rita I Csako David P Fernandez Elaine F Fernandez Hironobu Fujiwara Johannes Fuss Roman Gabrhelík Ateret Gewirtz-Meydan Biljana Gjoneska Mateusz Gola Joshua B Grubbs Hashim T Hashim Md Saiful Islam Mustafa Ismail Martha C Jiménez-Martínez Tanja Jurin Ondrej Kalina Verena Klein András Költő Chih-Ting Lee Sang-Kyu Lee Karol Lewczuk Chung-Ying Lin Christine Lochner Silvia López-Alvarado Kateřina Lukavská Percy Mayta-Tristán Dan J Miller Oľga Orosová Gábor Orosz Gabriel C Quintero Garzola Jano Ramos-Diaz Kévin Rigaud Ann Rousseau Marco De Tubino Scanavino Marion K Schulmeyer Pratap Sharan Mami Shibata Sheikh Shoib Vera Sigre-Leirós Luke Sniewski Ognen Spasovski Vesta Steibliene Dan J Stein Berk C Ünsal Marie-Pier Vaillancourt-Morel Marie Claire Van Hout Beáta Bőthe Departamento de Psicología y Filosofía Facultad de Ciencias Sociales Universidad de Tarapacá Arica Arica y Parinacota Chile. Electronic address: gquintanaz@academicos.uta.cl. Facultad de Psicología Universidad de Talca Chile. Departamento de Psicología y Filosofía Facultad de Ciencias Sociales Universidad de Tarapacá Arica Arica y Parinacota Chile. Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary. Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary. Department of Psychology University of Nevada Las Vegas Las Vegas NV USA. Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar Gibraltar. Yale University School of Medicine New Haven CT USA Connecticut Council on Problem Gambling Wethersfield CT USA. Departmento de Psicología Básica Clínica y Psicobiología University Jaume I of Castellón Spain. Institute for Behavioural Addictions Sigmund Freud University Vienna Austria. Département de psychologie Université de Montréal Montréal Canada. Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland. Institute for Sex Research Sexual Medicine and Forensic Psychiatry University Medical Centre Hamburg-Eppendorf Hamburg Germany. Laboratory of Behavioral Medicine Neuroscience Institute Lithuanian University of Health Sciences Palanga Lithuania. Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico. William James Center for Research Departamento de Educação e Psicologia Universidade de Aveiro Aveiro Portugal. Department of Personality Assessment and Psychological Treatments University of Valencia Spain. Department of Psychology College of Humanity and Social Science Fuz

BACKGROUND:Depression and anxiety are among the most prevalent mental health issues experienced worldwide. However, whereas cross-cultural studies utilize psychometrically valid and reliable scales, fewer can meaningfully compare these conditions across different groups. To address this gap, the current study aimed to psychometrically assess the Brief Symptomatology Index (BSI) in 42 countries. METHODS:Using data from the International Sex Survey (N = 82,243; M = 32.39; SD = 12.52; women: n = 46,874; 57 %), we examined the reliability of depression and anxiety symptom scores of the BSI-18, as well as evaluated evidence of construct, invariance, and criterion-related validity in predicting clinically relevant variables across countries, languages, genders, and sexual orientations. RESULTS:Results corroborated an invariant, two-factor structure across all groups tested, exhibiting excellent reliability estimates for both subscales. The 'caseness' criterion effectively discriminated among those at low and high risk of depression and anxiety, yielding differential effects on the clinical criteria examined. LIMITATIONS:The predictive validation was not made against a clinical diagnosis, and the full BSI-18 scale was not examined (excluding the somatization sub-dimension), limiting the validation scope of the BSI-18. Finally, the study was conducted online, mainly by advertisements through social media, ultimately skewing our sample towards women, younger, and highly educated populations. CONCLUSIONS:The results support that the BSI-12 is a valid and reliable assessment tool for assessing depression and anxiety symptoms across countries, languages, genders, and sexual orientations. Further, its caseness criterion can discriminate well between participants at high and low risk of depression and anxiety.

关键词： Anxiety Brief Symptom Inventory Cross-cultural Depression Measurement invariance Psychometric

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A multipoint guidance mechanism for β-barrel folding on the SAM complex

引用

Nature structural & molecular biology 2023年第2期30卷 233页

作者： Hironori Takeda Jon V Busto Caroline Lindau Akihisa Tsutsumi Kentaro Tomii Kenichiro Imai Yu Yamamori Takatsugu Hirokawa Chie Motono Iniyan Ganesan Lena-Sophie Wenz Thomas Becker Masahide Kikkawa Nikolaus Pfanner Nils Wiedemann Toshiya Endo Faculty of Life Sciences Kyoto Sangyo University Kyoto Japan. Nara Institute of Science and Technology Ikoma Japan. Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany. Department of Cell Biology and Anatomy Graduate School of Medicine The University of Tokyo Tokyo Japan. Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan. Cellular and Molecular Biotechnology Research Institute AIST Tokyo Japan. Division of Biomedical Science Faculty of Medicine University of Tsukuba Tsukuba Japan. Transborder Medical Research Center University of Tsukuba Tsukuba Japan. Computational Bio Big-Data Open Innovation Laboratory AIST Waseda University Tokyo Japan. Sanofi-Aventis Deutschland GmbH Frankfurt Germany. Institute of Biochemistry and Molecular Biology Faculty of Medicine University of Bonn Bonn Germany. Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany. nikolaus.pfanner@biochemie.uni-freiburg.de. CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Freiburg Germany. nikolaus.pfanner@biochemie.uni-freiburg.de. BIOSS Centre for Biological Signalling Studies University of Freiburg Freiburg Germany. nikolaus.pfanner@biochemie.uni-freiburg.de. Institute of Biochemistry and Molecular Biology Centre for Biochemistry and Molecular Cell Research Faculty of Medicine University of Freiburg Freiburg Germany. nils.wiedemann@biochemie.uni-freiburg.de. CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Freiburg Germany. nils.wiedemann@biochemie.uni-freiburg.de. BIOSS Centre for Biological Signalling Studies University of Freiburg Freiburg Germany. nils.wiedemann@biochemie.uni-freiburg.de. Faculty of Life Sciences Kyoto Sangyo University

来源：评论

学校读者我要写书评

暂无评论

The Past, Present, and Future of the Brain Imaging data Structure (BIDS)

arXiv

引用

arXiv 2023年

作者： Poldrack, Russell A. Markiewicz, Christopher J. Appelhoff, Stefan Ashar, Yoni K. Auer, Tibor Baillet, Sylvain Bansal, Shashank Beltrachini, Leandro Bénar, Christian G. Bertazzoli, Giacomo Bhogawar, Suyash Blair, Ross W. Bortoletto, Marta Boudreau, Mathieu Brooks, Teon L. Calhoun, Vince D. Castelli, Filippo Maria Clement, Patricia Cohen, Alexander L. Cohen-Adad, Julien D'Ambrosio, Sasha de Hollander, Gilles de la Iglesia-Vayá, María de la Vega, Alejandro Delorme, Arnaud Devinsky, Orrin Draschkow, Dejan Duff, Eugene Paul DuPre, Elizabeth Earl, Eric Esteban, Oscar Feingold, Franklin W. Flandin, Guillaume Galassi, Anthony Gallitto, Giuseppe Ganz, Melanie Gau, Rémi Gholam, James Ghosh, Satrajit S. Giacomel, Alessio Gillman, Ashley G. Gleeson, Padraig Gramfort, Alexandre Guay, Samuel Guidali, Giacomo Halchenko, Yaroslav O. Handwerker, Daniel A. Hardcastle, Nell Herholz, Peer Hermes, Dora Honey, Christopher J. Innis, Robert B. Ioanas, Horea-Ioan Jahn, Andrew Karakuzu, Agah Keator, David B. Kiar, Gregory Kincses, Balint Laird, Angela R. Lau, Jonathan C. Lazari, Alberto Legarreta, Jon Haitz Li, Adam Li, Xiangrui Love, Bradley C. Lu, Hanzhang Maumet, Camille Mazzamuto, Giacomo Meisler, Steven L. Mikkelsen, Mark Mutsaerts, Henk Nichols, Thomas E. Nikolaidis, Aki Nilsonne, Gustav Niso, Guiomar Norgaard, Martin Okell, Thomas W. Oostenveld, Robert Ort, Eduard Park, Patrick J. Pawlik, Mateusz Pernet, Cyril R. Pestilli, Franco Petr, Jan Phillips, Christophe Poline, Jean-Baptiste Pollonini, Luca Raamana, Pradeep Reddy Ritter, Petra Rizzo, Gaia Robbins, Kay A. Rockhill, Alexander P. Rogers, Christine Rokem, Ariel Rorden, Chris Routier, Alexandre Saborit-Torres, Jose Manuel Salo, Taylor Schirner, Michael Smith, Robert E. Spisak, Tamas Sprenger, Julia Swann, Nicole C. Szinte, Martin Takerkart, Sylvain Thirion, Bertrand Thomas, Adam G. Torabian, Sajjad Varoquaux, Gael Voytek, Bradley Welzel, Julius Wilson, Martin Yarkoni, Tal Gorgolewski, Krzysztof J. Department of Psychology Stanford University StanfordCA United States Max Planck Institute for Human Development Berlin Germany University of Colorado Anschutz Medical Campus AuroraCO United States School of Psychology University of Surrey Guildford United Kingdom McConnell Brain Imaging Centre Montréal Neurological Institute McGill University Montréal Canada Department of Bioengineering University of California San Diego La Jolla CA United States School of Physics Astronomy Cardiff University Wales United Kingdom Institut de Neurosciences des Systèmes Marseille France Neurophysiology Lab IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli Brescia Italy Center for Mind/Brain Sciences - CIMeC University of Trento TN Rovereto Italy Brigham and Women’s Hospital BostonMA United States Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Rackspace Technology San AntonioTX United States NeuroPoly Lab Polytechnique Montréal MontréalQC Canada Georgia State Georgia Tech Emory AtlantaGA United States University of Florence Sesto Fiorentino Italy Bioretics srl Cesena Italy Department of Medical Imaging Ghent University Hospital Ghent Belgium Department of Diagnostic Sciences Ghent University Ghent Belgium Department of Neurology Boston Children's Hospital BostonMA United States Dipartimento di Scienze della Salute dell'Università degli Studi di Milano Milan Italy Department of Clinical and Experimental Epilepsy University College London United Kingdom Zurich Center for Neuroeconomics Department of Economics University of Zurich Zurich Switzerland UMIB-FISABIO Valencia Spain The University of Texas at Austin AustinTX United States SCCN University of California San Diego La Jolla CA United States Department of Neurology NYU Langone Medical Center New YorkNY United States Department of Experimental Psychology University of Oxford Oxford United Kingdom UK Dementia Research Ins

The Brain Imaging data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS. © 2023, CC BY.

关键词： data structures

来源：评论

学校读者我要写书评

暂无评论

Biomedical image analysis competitions: The state of current participation practice

arXiv

引用

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

来源：评论

学校读者我要写书评

暂无评论

Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification

arXiv

引用

arXiv 2017年

作者： Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China School of Electronics and Information Technology Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Collaborative Innovation Center of High Performance Computing NUDT China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose unsupervised asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our model works much more suitable for unsupervised RE-ID compared to classical unsupervised metric learning models. We also compare with existing unsupervised REID methods, and our model outperforms them with notable margins. Specifically, we report the results on large-scale unlabelled RE-ID dataset, which is important but unfortunately less concerned in literatures. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

The short version of the Sexual Distress Scale (SDS-3): Measurement invariance across countries, gender identities, and sexual orientations

引用

International Journal of Clinical and Health Psychology 2024年第2期24卷 100461-100461页

作者： Lin, Chung-Ying Tsai, Meng-Che Koós, Mónika Nagy, Léna Kraus, Shane W. Demetrovics, Zsolt Potenza, Marc N. Ballester-Arnal, Rafael Batthyány, Dominik Bergeron, Sophie Billieux, Joël Briken, Peer Cárdenas-López, Georgina Carvalho, Joana Castro-Calvo, Jesús Chen, Lijun Ciocca, Giacomo Corazza, Ornella Csako, Rita I. Fernandez, David P. Fernandez, Elaine F. Fujiwara, Hironobu Fuss, Johannes Gabrhelík, Roman Gewirtz-Meydan, Ateret Gjoneska, Biljana Gola, Mateusz Grubbs, Joshua B. Hashim, Hashim T. Islam, Md. Saiful Ismail, Mustafa Jiménez-Martínez, Martha Jurin, Tanja Kalina, Ondrej Klein, Verena Költő, András Lee, Sang-Kyu Lewczuk, Karol Lochner, Christine López-Alvarado, Silvia Lukavská, Kateřina Mayta-Tristán, Percy Milea, Ion Miller, Dan J. Orosová, Oľga Orosz, Gábor Ponce, Fernando P. Quintana, Gonzalo R. Garzola, Gabriel C. Quintero Ramos-Diaz, Jano Rigaud, Kévin Rousseau, Ann Scanavino, Marco De Tubino Schulmeyer, Marion K. Sharan, Pratap Shibata, Mami Shoib, Sheikh Sigre-Leirós, Vera Sniewski, Luke Spasovski, Ognen Steibliene, Vesta Stein, Dan J. Strizek, Julian Štulhofer, Aleksandar Ünsal, Berk C. Vaillancourt-Morel, Marie-Pier Van Hout, Marie Claire Bőthe, Beáta Institute of Allied Health Sciences College of Medicine National Cheng Kung University Tainan Taiwan Biostatistics Consulting Center National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Department of Pediatrics National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Doctoral School of Psychology ELTE Eötvös Loránd University Budapest Hungary Institute of Psychology ELTE Eötvös Loránd University Budapest Hungary Department of Psychology University of Nevada Las Vegas NV United States Centre of Excellence in Responsible Gaming University of Gibraltar Gibraltar United Kingdom College of Education Psychology and Social Work Flinders University Adelaide Australia Yale University School of Medicine New Haven CT United States Connecticut Council on Problem Gambling Wethersfield CT United States Departamento de Psicología Básica Clínica y Psicobiología University Jaume I Castelló Spain Institute for Behavioural Addictions Sigmund Freud University Vienna Austria Département de Psychologie Université de Montréal Montréal Canada Centre de recherche interdisciplinaire sur les problèmes conjugaux et les agressions sexuelles Montréal Canada Institute of Psychology University of Lausanne Lausanne Switzerland Center for Excessive Gambling Addiction Medicine Lausanne University Hospitals (CHUV) Lausanne Switzerland Institute for Sex Research Sexual Medicine & Forensic Psychiatry University Medical Center Hamburg-Eppendorf (UKE) Hamburg Germany Virtual Teaching and Cyberpsychology Laboratory School of Psychology National Autonomous University of Mexico Mexico Mexico William James Center for Research Department of Education and Psychology University of Aveiro Aveiro Portugal Department of Personality Assessment and Psychological Treatments University of Valencia Valencia Spain Department of Psychology College of Humanity and Social Science Fuzhou University

Background: The three-item Sexual Distress Scale (SDS-3) has been frequently used to assess distress related to sexuality in public health surveys and research on sexual wellbeing. However, its psychometric properties and measurement invariance across cultural, gender and sexual subgroups have not yet been examined. This multinational study aimed to validate the SDS-3 and test its psychometric properties, including measurement invariance across language, country, gender identity, and sexual orientation groups. Methods: We used global survey data from 82,243 individuals (Mean _age=32.39 years;40.3 % men, 57.0 % women, 2.8 % non-binary, and 0.6 % other genders) participating in the International Sexual Survey (ISS;https://***/) across 42 countries and 26 languages. Participants completed the SDS-3, as well as questions regarding sociodemographic characteristics, including gender identity and sexual orientation. Results: Confirmatory factor analysis (CFA) supported a unidimensional factor structure for the SDS-3, and multi-group CFA (MGCFA) suggested that this factor structure was invariant across countries, languages, gender identities, and sexual orientations. Cronbach's α for the unidimensional score was 0.83 (range between 0.76 and 0.89), and McDonald's ω was 0.84 (range between 0.76 and 0.90). Participants who did not experience sexual problems had significantly lower SDS-3 total scores (M = 2.99;SD=2.54) compared to those who reported sexual problems (M = 5.60;SD=3.00), with a large effect size (Cohen's d = 1.01 [95 % CI=-1.03, -0.98];p < 0.001). Conclusion: The SDS-3 has a unidimensional factor structure and appears to be valid and reliable for measuring sexual distress among individuals from different countries, gender identities, and sexual orientations. © 2024 The Author(s)

关键词： Confirmatory factor analysis (CFA) International sex survey Psychometric analysis Sexual distress

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：