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arXiv 2023年

作者： Loughran, B. streeter, M.J.V. Ahmed, H. Astbury, s. Balcazar, M. Borghesi, M. Bourgeois, N. Curry, C.B. Dann, s.J.D. Diiorio, s. Dover, N.P. Dzelzanis, t. Ettlinger, O.C. Gauthier, M. Giuffrida, L. Glenn, G.D. Glenzer, s.H. Green, J.s. Gray, R.J. Hicks, G.s. Hyland, C. istokskaia, V. King, M. Margarone, D. McCusker, O. McKenna, P. Najmudin, Z. Parisuaña, C. Parsons, P. spindloe, C. symes, D.R. thomas, A.G.R. treffert, F. Xu, N. Palmer, C.A.J. School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom Central Laser Facility STFC Rutherford Appleton Laboratory DidcotOX11 0QX United Kingdom Gérard Mourou Center for Ultrafast Optical Science University of Michigan Ann ArborMI48109-2099 United States SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo ParkCA United States Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G1H9 Canada The John Adams Institute for Accelerator Science Imperial College London LondonSW7 2AZ United Kingdom ELI Beamlines Centre Institute of Physics Czech Academy of Sciences Za Radnicí 835 Dolní Břežany252 41 Czech Republic Department of Applied Physics Stanford University StanfordCA94305 United States Department of Physics SUPA University of Strathclyde GlasgowG4 0NG United Kingdom Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Prague Czech Republic Department of Mechanical Engineering Stanford University StanfordCA94305 United States Institut für Kernphysik Technische Universität Darmstadt Karolinenplatz 5 Darmstadt64289 Germany

the interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. this limits the utility of sequential 1D scanning of experimental parameters for the optimisation of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimisation. Here, an automated, HRR-compatible system produced high fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimisation of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually-optimized laser pulses but using only 60% of the laser energy. this demonstration of automated optimisation of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources. © 2023, CC BY.

关键词： Proton beams

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Densenet for breast tumor classification in mammographic images

arXiv

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arXiv 2021年

作者： Gaona, Yuliana Jiménez Rodriguez-Alvarez, María José Morató, Hector Espinó Malla, Darwin Castillo Lakshminarayanan, Vasudevan Departamento de Química y Ciencias Exactas Universidad Técnica Particular de Loja San Cayetano Alto s/n LojaCP1101608 Ecuador Instituto de Instrumentación para la Imagen Molecular I3M Universitat Politécnica de Valencia ValenciaE-46022 Spain Theoretical and Experimental Epistemology Lab School of Optometry and Vision Science Department of Systems Design Engineering Physics and Electrical and Computer Engineering University of Waterloo WaterlooONN2L3G1 Canada

Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. the common approaches of screening for women are tomosynthesis and mammography images. However, the traditional manual diagnosis requires an intense workload by pathologists, who are prone to diagnostic errors. thus, the aim of this study is to build a deep convolutional neural network method for automatic detection, segmentation, and classification of breast lesions in mammography images. Based on deep learning the Mask-CNN (RoiAlign) method was developed to features selection and extraction;and the classification was carried out by DenseNet architecture. Finally, the precision and accuracy of the model is evaluated by cross validation matrix and AUC curve. to summarize, the findings of this study may provide a helpful to improve the diagnosis and efficiency in the automatic tumor localization through the medical image classification. © 2021, CC BY.

关键词： Convolutional neural networks

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smart health assist for elder people using iot

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AiP Conference Proceedings 2022年第1期2405卷

作者： V. Arulkumar i. Chandra D. subitha A. Raja R. Ganesan Rajasekhar Atla 1Department of Information Technology SSN College of Engineering Kalavakkam TN 603110 India 2Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai TN 600124 India 3Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science and Technology Chennai TN 600062 India 4Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai TN 602105 India 5Department of Biomedical Engineering G. S. Pillay Engineering College Nagapattinam TN 611002 India 6Department of Electronics and Communication Engineering N.B.K.R.I.S.T Vidyanagar AP 524413 India

internet of things is a one which is emerged by advancement in the information and communication technology. And it is a new technology that connects the things from various domains through the internet and gives more convenience to several sectors. in the medical field, the iot play an important role to monitor the patient’s health from a remote location and healthcare is an essential part of the life. this paper proposes how to design and monitor the elder’s health from a remote location. the parameters that are measured by elder include temperature, glucose level, blood, pulse rate. the glucose level is measured by a non-invasive method. this parameter values are displayed in the LCD screen for elder reference and these values are uploaded to cloud frequently for future reference. Each parameter is assigned to a threshold value if it exceeds the threshold value, a buzzer and LED intimation is given to the elder and text message was sent to the caretaker person and to the nearby hospital in case of emergency.

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A core outcome set for future male infertility research: development of an international consensus

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Fertility and sterility 2025年第6期123卷 1017-1028页

作者： Rimmer, Michael P. Howie, Ruth A. Anderson, Richard A. Barratt, Christopher L.R. Barnhart, Kurt t. Beebeejaun, Yusuf Bertolla, Ricardo Pimenta Bortoletto, Pietro Brannigan, Robert E. Cantineau, Astrid E.P. Caroppo, Ettore Collura, Barbara L. Coward, Kevin Duncan, William Colin Eisenberg, Michael L. Gellatly, steven A. Geyter, Christian De Goulis, Dimitrios G. Henkel, Ralf R. Ho, Vu N.A. Hussein, Alayman F. Huyser, Carin Kadijk, Jozef H. Kamath, Mohan s. Khashaba, shadi Khattak, Hajra Kobori, Yoshitomo Kopeika, Julia Kucuk, tansu Luján, saturnino Matsaseng, thabo Christopher Mathur, Raj s. McEleny, Kevin Mitchell, Rod t. Mol, Ben W. Murage, Alfred M. Ng, Ernest H.Y. Pacey, Allan Perheentupa, Antti H. Du Plessis, stefan Rives, Nathalie sarris, ippokratis schlegel, Peter N. shabbir, Majid Śmiechowski, Maciej subramanian, Venkatesh sunkara, sesh K. tarlarzis, Basil C. tüttelmann, Frank Vail, Andy van Wely, Madelon Vazquez-Levin, Mónica H. Lan N. Vuong Wang, Alex Y. Wang, Rui Duffy, James M.N. Farquhar, Cindy M. Niederberger, Craig Centre for Reproductive Health Institute of Regeneration and Repair University of Edinburgh Edinburgh United Kingdom Edinburgh Fertility Centre Simpsons Centre for Reproductive Health Royal Infirmary of Edinburgh Edinburgh United Kingdom Reproductive Medicine Research Group Ninewells Hospital and Medical School University of Dundee Dundee United Kingdom Department of Obstetrics and Gynaecology University of Pennsylvania School of Medicine Philadelphia PA United States King's Fertility The Fetal Medicine Research Institute King's College London London United Kingdom Division of Urology Department of Surgery Universidade Federal de Sao Paulo Sao Paulo Brazil Department of Obstetrics and Gynecology School of Medicine C.S. Mott Center for Human Growth and Development Wayne State University Detroit MI United States Boston IVF Beth Israel Deaconess Medical Center Boston MA United States Feinberg School of Medicine Northwestern University Chicago IL United States Centre of Reproductive Medicine University of Groningen University Medical Centre Groningen Groningen Netherlands ASL Bari Andrology Outpatients Clinic Conversano Italy RESOLVE: The National Infertility Association United States Nuffield Department of Women's & Reproductive Health University of Oxford Level 3 Women's Centre John Radcliffe Hospital Oxford United Kingdom Stanford University Stanford CA United States Reproductive Medicine and Gynaecological Endocrinology (RME) University Hospital University of Basel Basel Switzerland Units of Human Reproduction and Reproductive Endocrinology 1st Department of Obstetrics and Gynaecology Medical School Aristotle University of Thessaloniki Thessaloniki Greece Department of Digestion Metabolism and Reproduction Imperial College London London United Kingdom Department of Medical Bioscience University of the Western Cape Bellville South Africa LogixX Pharma Ltd. Theale United Kingdom IVFMD and HOPE Research Centre My Duc Hospital Ho Chi Minh City

Objective: to develop a core outcome set for male infertility trials. Design: A two-round Delphi survey and consensus development workshop were undertaken with healthcare professionals, researchers and clinicians globally. subjects: 334 participants from 39 countries participated in the Delphi survey, while 44 participants from 21 countries participated in the consensus development workshop. intervention or Exposure: NA Main Outcome Measures: the core outcome set for male infertility trials has been developed by the inclusion of specific male-factor outcomes in addition to the general infertility core outcome set which focuses on female-factor outcomes. Results: the outcomes identified include assessment of semen using the World Health Organisation recommendations for semen analysis;viable intrauterine pregnancy confirmed by ultrasound (accounting for singleton, twin and higher multiple pregnancies);pregnancy loss (accounting for ectopic pregnancy, miscarriage, stillbirth and termination of pregnancy);live birth;gestational age at delivery;birthweight;neonatal mortality;and major congenital anomaly. Although not a requirement as part of the core outcome set, other outcomes were identified as potentially useful in certain study settings. Conclusion: Embedding the core outcome set within RCts and systematic reviews should ensure the comprehensive selection, collection and reporting of core outcomes, which are inconsistently reported at present. Research funding bodies, the standard Protocol items: Recommendations for interventional trials (sPiRit) statement, and over 80 specialty journals, including the Cochrane Gynaecology and Fertility Group, Fertility and sterility and Human Reproduction, have committed to implementing this core outcome set for male infertility trials. trial Registration Number: Core Outcome Measures in Effectiveness trials (COMEt) initiative registration No: 1586. Available at ***/studies/Details/1586. © 2025 [Author/Employing

关键词： consensus development study core outcome sets Delphi survey male infertility outcome measures outcomes randomized control trials

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Why is the Winner the Best?

Why is the Winner the Best?

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. tizabi F. isensee t. J. Adler s. Ali V. Andrearczyk M. Aubreville U. Baid s. Bakas N. Balu s. Bano J. Bernal s. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis s. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-isla B. Menze A. Noble V. Oreiller N. Padoy s. Pati K. Payette t. Rädsch J. Rafael-Patiño V. singh Bawa s. speidel C. H. sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou i. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou s. Hong M. Jahanifar i. Jang A. Kadkhodamohammadi i. Kang F. Kofler s. Kondo H. Kuijf M. Li M. Luu t. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen s. Pang J. Park s. Park s. Płotka E. Puybareau N. Rajpoot K. Ryu N. saeed A. shephard P. shi D. Štepec R. subedi G. tochon H. R. torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong s. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? to address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of iEEE isBi 2021 and MiCCAi 2021. statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. these typically include the use of multi.t.sk learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). the “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. the insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Enabling Research through the sCiP Optimization suite 8.0

arXiv

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arXiv 2023年

作者： Bestuzheva, Ksenia Besançon, Mathieu Chen, Wei-Kun Chmiela, Antonia Donkiewicz, tim van Doornmalen, Jasper Eifler, Leon Gaul, Oliver Gamrath, Gerald Gleixner, Ambros Gottwald, Leona Graczyk, Christoph Halbig, Katrin Hoen, Alexander Hojny, Christopher van der Hulst, Rolf Koch, thorsten Lübbecke, Marco Maher, stephen J. Matter, Frederic Mühmer, Erik Müller, Benjamin Pfetsch, Marc E. Rehfeldt, Daniel schlein, steffan schlösser, Franziska serrano, Felipe shinano, Yuji sofranac, Boro turner, Mark Vigerske, stefan Wegscheider, Fabian Wellner, Philipp Weninger, Dieter Witzig, Jakob Zuse Institute Berlin Department AIS2T Takustr. 7 Berlin14195 Germany ZIB-AIS2T Germany School of Mathematics and Statistics Beijing Institute of Technology Beijing100081 China RWTH Aachen University Lehrstuhl für Operations Research Kackertstr. 7 Aachen52072 Germany Eindhoven University of Technology Department of Mathematics and Computer Science Netherlands ZIB-AIS2T I2DAMO GmbH Englerallee 19 Berlin14195 Germany ZIB-AIS2T HTW Berlin Germany Friedrich-Alexander Universität Erlangen-Nürnberg Department of Data Science Cauerstr. 11 Erlangen91058 Germany University of Twente Department of Discrete Mathematics and Mathematical Programming P.O. Box 217 Enschede7500 AE Netherlands Technische Universität Berlin Software and Algorithms for Discrete Optimization Straße des 17. Juni 135 Berlin10623 Germany ZIB Department A2IM Takustr. 7 Berlin14195 Germany RWTH Aachen-Lehrstuhl für OR Germany University of Exeter College of Engineering Mathematics and Physical Sciences Exeter United Kingdom Technische Universität Darmstadt Fachbereich Mathematik Dolivostr. 15 Darmstadt64293 Germany TU Darmstadt Fachbereich Mathematik Germany ZIB-A2IM Germany TU Berlin Germany ZIB-A2IM Software and Algorithms for Discrete Optimization Institute of Mathematics TU Berlin Germany GAMS Software GmbH c/o ZIB-AIS2T Germany

the sCiP Optimization suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework sCiP. the focus of this paper is on the role of the sCiP Optimization suite in supporting research. sCiP's main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of sCiP's application as a research tool and as a platform for further developments. Further, the paper gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon *** Codes 90C05, 90C10, 90C11, 90C30, 90C90, 65Y05 Copyright © 2023, the Authors. All rights reserved.

关键词： integer programming

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Exploring End-to-end Differentiable Neural Charged Particle tracking – A Loss Landscape Perspective

arXiv

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arXiv 2024年

作者： Kortus, tobias Keidel, Ralf Gauger, Nicolas R. Aehle, Max Alme, Johan Barnaföldi, Gergely Gábor Bodova, tea Borshchov, Vyacheslav van den Brink, Anthony Chaar, Mamdouh Eikeland, Viljar Feofilov, Gregory Garth, Christoph Gauger, Nicolas R. Genov, Georgi Grøttvik, Ola Helstrup, Håvard igolkin, sergey Keidel, Ralf Kobdaj, Chinorat Kortus, tobias Leonhardt, Viktor Mehendale, shruti Mulawade, Raju Ningappa Odland, Odd Harald O’Neill, George Papp, Gábor Peitzmann, thomas Pettersen, Helge Egil seime Piersimoni, Pierluigi Protsenko, Maksym Rauch, Max Rehman, Attiq Ur Richter, Matthias Röhrich, Dieter santana, Joshua schilling, Alexander seco, Joao songmoolnak, Arnon sudár, Ákos sølie, Jarle Rambo tambave, Ganesh tymchuk, ihor Ullaland, Kjetil Varga-Kofarago, Monika Wagner, Boris Xiao, RenZheng Yang, shiming Yokoyama, Hiroki Germany TU Kaiserslautern Kaiserslautern67663 Germany Department of Physics and Technology University of Bergen Bergen5007 Norway Wigner Research Centre for Physics Budapest Hungary Research and Production Enterprise"LTU" [RPELTU Kharkiv Ukraine Institute for Subatomic Physics Utrecht University/Nikhef Utrecht Netherlands St. Petersburg University St. Petersburg Russia Scientific Visualization Lab TU Kaiserslautern Kaiserslautern67663 Germany Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen5020 Norway Center for Technology and Transfer [ZTT University of Applied Sciences Worms Worms Germany Institute of Science Suranaree University of Technology Nakhon Ratchasima Thailand Department of Oncology and Medical Physics Haukeland University Hospital Bergen5021 Norway Institute for Physics Eötvös Loránd University 1/A Pázmány P. Sétány BudapestH-1117 Hungary UniCamillus – Saint Camillus International University of Health Sciences Rome Italy Department of Physics University of Oslo Oslo0371 Norway Department of Biomedical Physics in Radiation Oncology DKFZ—German Cancer Research Center Heidelberg Germany Department of Physics and Astronomy Heidelberg University Heidelberg Germany Budapest University of Technology and Economics Budapest Hungary Department of Diagnostic Physics Division of Radiology and Nuclear Medicine Oslo University Hospital Oslo Norway Center for Medical and Radiation Physics [CMRP National Institute of Science Education and Research [NISER Bhubaneswar India College of Mechanical & Power Engineering China Three Gorges University Yichang China

Measurement and analysis of high energetic particles for scientific, medical or industrial applications is a complex procedure, requiring the design of sophisticated detector and data processing systems. the development of adaptive and differentiable software pipelines using a combination of conventional and machine learning algorithms is therefore getting ever more important to optimize and operate the system efficiently while maintaining end-to-end (E2E) differentiability. We propose for the application of charged particle tracking an E2E differentiable decision-focused learning scheme using graph neural networks with combinatorial components solving a linear assignment problem for each detector layer. We demonstrate empirically that including differentiable variations of discrete assignment operations allows for efficient network optimization, working better or on par with approaches that lack E2E differentiability. in additional studies, we dive deeper into the optimization process and provide further insights from a loss landscape perspective. We demonstrate that while both methods converge into similar performing, globally well-connected regions, they suffer under substantial predictive instability across initialization and optimization methods, which can have unpredictable consequences on the performance of downstream tasks such as image reconstruction. We also point out a dependency between the interpolation factor of the gradient estimator and the prediction stability of the model, suggesting the choice of sufficiently small values. Given the strong global connectivity of learned solutions and the excellent training performance, we argue that E2E differentiability provides, besides the general availability of gradient information, an important tool for robust particle tracking to mitigate prediction instabilities by favoring solutions that perform well on downstream tasks. Copyright © 2024, the Authors. All rights reserved.

关键词： Charged particles

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Detecting Glaucoma in the Ocular Hypertension study Using Deep Learning

TechRxiv

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techRxiv 2021年

作者： Fan, Rui Bowd, Christopher Christopher, Mark Brye, Nicole Proudfoot, James A. Rezapour, Jasmin Belghith, Akram Goldbaum, Michael H. Chuter, Benton Girkin, Christopher A. Fazio, Massimo A. Liebmann, Jeffrey M. Weinreb, Robert N. Gordon, Mae O. Kass, Michael A. Kriegman, David Zangwill, Linda M. Hamilton Glaucoma Center Viterbi Family Department of Ophthalmology Shiley Eye Institute University of California La Jolla San DiegoCA United States Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Ophthalmology Universitätsmedizin der Johannes Gutenberg-Universität Mainz Rheinland-Pfalz Mainz Germany Department of Ophthalmology School of Medicine The University of Alabama at Birmingham BirminghamAL United States Department of Biomedical Engineering School of Engineering The University of Alabama at Birmingham BirminghamAL United States Bernard and Shirlee Brown Glaucoma Research Laboratory Edward S. Harkness Eye Institute Columbia University Medical Center New YorkNY United States Department of Ophthalmology and Visual Sciences Washington University School of Medicine Washington University St. LouisMO United States

importance: Automated deep learning (DL) analyses of fundus photographs potentially can reduce the cost and improve the efficiency of reading center assessment of endpoints in clinical trials. Objective: to investigate the diagnostic accuracy of DL algorithms trained on fundus photographs from the Ocular Hypertension treatment study (OHts) to detect primary open-angle glaucoma (POAG). study Design: Diagnostic/prognostic study setting and Participants: 1,636 OHts participants from 22 sites with a mean follow-up (range) of 10.7 (0.0, 14.3) years. 66,715 photographs from 3,272 eyes were used to train and test a ResNet-50 model to detect the OHts Endpoint Committee POAG determination based on optic disc (n=287 eyes, 3,502 photographs) and/or visual field (n=198 eyes, 2,300 visual fields) changes. three independent test sets were used to evaluate the generalizability of the model. Main Outcome and Measures: Areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities were calculated to compare model performance. Evaluation of false-positive rates was used to determine whether the DL model detected POAG before the Endpoint Committee POAG determination. Results: the DL model achieved an AUROC (95% Ci) of 0.88 (0.82, 0.92) for the overall OHts POAG endpoint. For the OHts endpoints based on optic disc changes or visual field changes, AUROCs were 0.91 (0.88, 0.94) and 0.86 (0.76, 0.93), respectively. False-positive rates (at 90% specificity) were higher in photographs of eyes that later developed POAG by disc or visual field (19.1%) compared to eyes that did not develop POAG (7.3%) during their follow-up. the diagnostic accuracy of the DL model developed on the optic disc endpoint applied to 3 independent datasets was lower with AUROC ranging from 0.74 to 0.79. Conclusion and Relevance: the model’s high diagnostic accuracy using OHts photographs suggests that DL has the potential to standardize and automate POAG determination for cl

关键词： Ophthalmology

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Visualizing and Understanding Customized Convolutional Neural Network for Recognition of Handwritten Marathi Numerals

Visualizing and Understanding Customized Convolutional Neura...

引用

2018 international Conference on Computational intelligence and Data science, iCCiDs 2018

作者： Mane, D.t. Kulkarni, U.V. Department of Computer Science and Engineering S.G.G.S.I.E. and T. Nanded Maharashtra431606 India

Numeral recognition is one of the most indispensable applications in pattern recognition. Recognizing numerals, written in indian languages is a demanding problem. Devanagari Marathi is one such popular indian language script, and perceiving Marathi numerals written in different patterns, is a challenging task. Depending on the type of feature extraction, varied approaches dealing with numeral recognition, have been suggested and practiced on smaller data-sets. However, no standard large data-set is available for handwritten Marathi numerals. therefore, a data-set with 80000 samples has been prepared for proposed work. this paper proposes a Customized Convolutional Neural Network (CCNN) that has the ability to learn the features automatically and predict the class of numerals from a wide ranged data-set. Additionally, visualization of the intermediate CCNN layers is presented that explains the dynamics of the presented network. Out of 80000 numerals, written in Marathi, 70000 samples are used for training and 10000, for testing. the CCNN's performance when verified using K- fold cross validation has achieved average 94.93% accuracy for testing data-sets. © 2018 the Authors. Published by Elsevier Ltd.

关键词： Convolution

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study of Ds1(2460)+→Ds+π+π- in B→D‾(*)Ds+π+π- decays

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science Bulletin 2025年第9期70卷 1432-1444页

作者： Aaij, R. Abdelmotteleb, A.s.W. Beteta, C.Abellan Abudinén, F. Ackernley, t. Adefisoye, A.A. Adeva, B. Adinolfi, M. Adlarson, P. Agapopoulou, C. Aidala, C.A. Ajaltouni, Z. Akar, s. Akiba, K. Albicocco, P. Albrecht, J. Alessio, F. Alexander, M. Aliouche, Z. Cartelle, P.Alvarez Amalric, R. Amato, s. Amey, J.L. Amhis, Y. An, L. Anderlini, L. Andersson, M. Andreianov, A. Andreola, P. Andreotti, M. Andreou, D. Anelli, A. Ao, D. Archilli, F. Argenton, M. Cuendis, s.Arguedas Artamonov, A. Artuso, M. Aslanides, E. Da silva, R.Ataíde Atzeni, M. Audurier, B. Bacher, D. Perea, i.Bachiller Bachmann, s. Bachmayer, M. Back, J.J. Rodriguez, P.Baladron Balagura, V. Balboni, A. Baldini, W. Balzani, L. Bao, H. de souza Leite, J.Baptista Pretel, C.Barbero Barbetti, M. Barbosa, i.R. Barlow, R.J. Barnyakov, M. Barsuk, s. Barter, W. Bartolini, M. Bartz, J. Basels, J.M. Bashir, s. Bassi, G. Batsukh, B. Battista, P.B. Bay, A. Beck, A. Becker, M. Bedeschi, F. Bediaga, i.B. Behling, N.B. Belin, s. Belous, K. Belov, i. Belyaev, i. Benane, G. Bencivenni, G. Ben-Haim, E. Berezhnoy, A. Bernet, R. Andres, *** Bertolin, A. Betancourt, C. Betti, F. Bex, J. Bezshyiko, ia. Bhom, J. Bieker, M.s. Biesuz, N.V. Billoir, P. Biolchini, A. Birch, M. Bishop, F.C.R. Bitadze, A. Bizzeti, A. Blake, t. Blanc, F. Blank, J.E. Blusk, s. Bocharnikov, V. Boelhauve, J.A. Garcia, O.Boente Boettcher, t. Bohare, A. Boldyrev, A. Bolognani, C.s. School of Physics and Astronomy Monash University Melbourne Australia Rio de Janeiro Brazil Rio de Janeiro Brazil Department of Engineering Physics Tsinghua University Beijing China Beijing China School of Physics State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China University of Chinese Academy of Sciences Beijing China Institute of Particle Physics Central China Normal University Hubei Wuhan China San Jose Costa Rica Université Savoie Mont Blanc CNRS AnnecyIN2P3-LAPP France Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Gif-Sur-Yvette France Aix Marseille Univ CNRS/IN2P3 CPPM Marseille France Université Paris-Saclay CNRS/IN2P3 IJCLab Orsay France Laboratoire Leprince-Ringuet CNRS/IN2P3 Ecole Polytechnique Institut Polytechnique de Paris Palaiseau France LPNHE Sorbonne Université Paris Diderot Sorbonne Paris Cité CNRS/IN2P3 Paris France I. Physikalisches Institut RWTH Aachen University Aachen Germany Universität Bonn-Helmholtz-Institut für Strahlen und Kernphysik Bonn Germany Technische Universität Dortmund Fakultät Physik Dortmund Germany Physikalisches Institut Albert-Ludwigs-Universität Freiburg Freiburg Germany Heidelberg Germany Ruprecht-Karls-Universität Heidelberg Physikalisches Institut Heidelberg Germany School of Physics University College Dublin Dublin Iceland INFN Sezione di Bari Bari Italy INFN Sezione di Bologna Bologna Italy INFN Sezione di Ferrara Ferrara Italy INFN Sezione di Firenze Firenze Italy INFN Laboratori Nazionali di Frascati Frascati Italy INFN Sezione di Genova Genova Italy INFN Sezione di Milano Milano Italy INFN Sezione di Milano-Bicocca Milano Italy INFN Sezione di Cagliari Monserrato Italy INFN Sezione di Padova Padova Italy INFN Sezione di Perugia Perugia Italy INFN Sezione di Pisa Pisa Italy INFN Sezione di Roma La Sapienza Roma Italy INFN Sezione di Roma Tor Vergata Roma Italy Nikhef National Institute for Subatomic Physi

An amplitude analysis of the Ds1(2460)+→Ds+π+π- transition is performed simultaneously in B0→D-Ds+π+π-, B+→D‾0Ds+π+π-, and B0→D*-Ds+π+π- decays. the study is based on a data sample of proton-proton collisions recorded with the LHCb detector at centre-of-mass energies of s=7,8, and 13 teV, corresponding to a total integrated luminosity of 9fb-1. A clear double-peak structure is observed in the m(π+π-) spectrum of the Ds1(2460)+→Ds+π+π- decay. the data can be described either with a model including f0500,f0980, and f2(1270) resonances, in which the contributions of f0(980) and f2(1270) are unexpectedly large, or with a model including f0(500), a doubly charged open-charm tetraquark state tcs¯++ and its isospin partner tcs¯0. if the former is considered implausible, the tcs¯ states are observed with high significance, and the data are consistent with isospin symmetry. When imposing isospin constraints between the two tcs¯ states, their mass and width are determined to be (2327±13±13) MeV and (96±16-23+170) MeV, respectively, where the first uncertainty is statistical and the second is systematic. the mass is slightly below the DK threshold, and a spin-parity of 0+ is favoured with high significance. © 2025 science China Press

关键词： Colliding beam accelerators

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