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Estimation of Body Mass Index from photographs using deep Convolutional Neural Networks

Informatics in Medicine Unlocked 2021年 26卷

作者： Pantanowitz, A. Cohen, E. Gradidge, P. Crowther, N.J. Aharonson, V. Rosman, B. Rubin, D.M. Biomedical Engineering Research Group School of Electrical & Information Engineering University of the Witwatersrand Johannesburg South Africa CNRS UMR 7178 «Institut Pluridisciplinaire Hubert CURIEN » université de Strasbourg Strasbourg France MRC/Wits Developmental Pathways for Health Research Unit Department of Paediatrics Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa CNRS UMR 7206 «Eco-anthropologie » Muséum National d'Histoire Naturelle Paris France Centre for Exercise Science and Sports Medicine Faculty of Health Sciences University of the Witwatersrand Johannesburg South Africa Department of Chemical Pathology National Health Laboratory Service and University of the Witwatersrand Johannesburg South Africa School of Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa

Obesity is an important concern in public health, and Body Mass Index is one of the useful, common and convenient measures. However, Body Mass Index requires access to accurate scales and a stadiometer for measurements, and could be made more convenient through analysis of photographs. It could be applied to photographs comprising more than one individual, leading to population screening. We use Convolutional Neural Networks to determine Body Mass Index from photographs in a study with 161 participants. The relatively low number of participants in the data, a common problem in medicine, is addressed by reducing the information in the photographs by generating silhouette images. We successfully determine Body Mass Index for unseen test data with high correlation between prediction and actual values, with correlation measurements of greater than 0.93 and a mean absolute error of 1.20. © 2021

关键词： Anthropomorphism Body Mass Index computer vision Deep Convolutional Neural Networks Machine learning

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Improving marker-based tracking for augmented reality in underwater environments

Improving marker-based tracking for augmented reality in und...

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2018 Eurographics Workshop on Graphics and Cultural Heritage, GCH 2018

作者： Cejka, Jan Marek, U. Agrafiotis, Panagiotis Skarlatos, Dimitrios Bruno, Fabio Liarokapis, Fotis Human Computer Interaction Laboratory Faculty of Informatics Masaryk University Czech Republic Photogrammetric Vision Laboratory Department of Civil Engineering and Geomatics Cyprus University of Technology Cyprus Cyprus Research S.r.l. - University of Calabria Italy

ISBN: (纸本)9783038680574

Underwater sites are a challenging environment for augmented reality. Images taken under water are degraded in several ways, most importantly they are affected by unbalanced colors due to uneven absorption of light in each color channel, blurring and desaturation caused by turbidity, or noise due to the presence of larger organisms, plants, or bubbles. In this paper, we introduce a new method based on white balancing that enhances underwater images to improve the results of detection of markers. We compare this method with several image enhancement methods, and evaluate their performance when applied to the problem of detecting markers under water. Our results show that our method improves the detection in underwater environments while keeping the computation time low. © 2018 GCH 2018 - Eurographics Workshop on Graphics and Cultural Heritage. All rights reserved.

关键词： Augmented reality

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer vision

arXiv

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arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 2 — Ex vivo imaging: added value and acquisition

arXiv

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arXiv 2022年

作者： Schilling, Kurt G. Grussu, Francesco Ianus, Andrada Hansen, Brian Howard, Amy F.D. Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Allan Johnson, G. Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Re

The value of preclinical diffusion MRI (dMRI) is substantial. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages including higher signal-to-noise ratio (SNR) and spatial resolution compared to in vivo studies, and enabling more advanced diffusion contrasts for improved microstructure and connectivity characterization. Another major advantage of ex vivo dMRI is the direct comparison with histological data, as a crucial methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work represents "Part 2" of a 3-part series of recommendations and considerations for preclinical dMRI. We describe best practices for dMRI of ex vivo tissue, with a focus on the value that ex vivo imaging adds to the field of dMRI and considerations in ex vivo image acquisition. We first give general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in specimens and models and discuss why some may be more or less appropriate for different studies. We then give guidelines for ex vivo protocols, including tissue fixation, sample preparation, and MR scanning. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. An overarching goal herein is to enhance the rigor and reproducibility of ex vivo dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY.

关键词： Image acquisition

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Considerations and Recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 — In vivo small-animal imaging

arXiv

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arXiv 2022年

作者： Jelescu, Ileana O. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio de Looij, Yohan van Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Acqua, Flavio Dell Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Schilling, Kurt G. Department of Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Resonance National HIgh Magnetic Field Laboratory TallahasseeFL United States Department of Neuropsychology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany USC Stevens Neuroimaging and Informatics Instit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected recommendations and guidelines from the diffusion community, on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We then give guidelines for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including pre-processing, model-fitting, and tractography. Finally, we provide an online resource which lists publicly available preclinical dMRI datasets and software packages, to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. While we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal herein is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge. © 2022, CC BY-NC-ND.

关键词： Invertebrates

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Aggregation Signature for Small Object Tracking

arXiv

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arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Yang, Jinyu Murino, Vittorio Zhang, Baochang Han, Jungong Guo, Guodong School of Electrical and Information Engineering Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China School of Computer Science University of Birmingham British United Kingdom University of Verona Verona Italy Pattern Analysis and Computer Vision department Istituto Italiano di Tecnologia Genoa Italy School of Automation Science and Electrical Engineering Beihang University Beijing China Shenzhen Academy of Aerospace Technology Shenzhen China WMG Data Science Group University of Warwick CoventryCV4 7AL United Kingdom Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

—Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://***/bczhangbczhang/. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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Deep learning enabled strain mapping of single-atom defects in 2D transition metal dichalcogenides with sub-picometer precision

arXiv

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arXiv 2020年

作者： Lee, Chia-Hao Khan, Abid Luo, Di Santos, Tatiane P. Shi, Chuqiao Janicek, Blanka E. Kang, Sangmin Zhu, Wenjuan Sobh, Nahil A. Schleife, André Clark, Bryan K. Huang, Pinshane Y. Department of Materials Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Physics University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign UrbanaIL61801 United States Materials Research Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

2D materials offer an ideal platform to study the strain fields induced by individual atomic defects, yet challenges associated with radiation damage have so-far limited electron microscopy methods to probe these atomic-scale strain fields. Here, we demonstrate an approach to probe single-atom defects with sub-picometer precision in a monolayer 2D transition metal dichalcogenide, WSe2-2xTe2x. We utilize deep learning to mine large datasets of aberration-corrected scanning transmission electron microscopy images to locate and classify point defects. By combining hundreds of images of nominally identical defects, we generate high signal-to-noise class-averages which allow us to measure 2D atomic coordinates with up to 0.3 pm precision. Our methods reveal that Se vacancies introduce complex, oscillating strain fields in the WSe2-2xTe2xlattice which cannot be explained by continuum elastic theory. These results indicate the potential impact of computer vision for the development of high-precision electron microscopy methods for beam-sensitive materials. Copyright © 2020, The Authors. All rights reserved.

关键词： Transition metals

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A Decentralized Multi-Agent System for Management of En Route Electric Vehicles

A Decentralized Multi-Agent System for Management of En Rout...

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IEEE SoutheastCon

作者： Abla O. Hariri Mohamad El Hariri Tarek Youssef Osama Mohammed Energy Systems Research Laboratory Department of Electrical and Computer Engineering Florida International University Miami Florida USA

ISBN: (纸本)9781538661345

Being energy producers and consumers (prosumers), electric vehicles introduce new challenges to the operation of the electricity grid. With the anticipated increase of the market for electric vehicles in the near future, systems that set rules to manage and organize their large-scale penetration into the power grid are needed. As the Internet of Things (IoT) vision evolves, the Internet of Electric Vehicles (IoEV) shall be a stepping-stone for establishing a link between the well-known hierarchical smart grid control architecture (primary, secondary, and tertiary control) and the emerging layer of the EVs as dynamic and mobile energy prosumers. Against this background, this paper proposes a decentralized multi-agent system designed for the management of en route connected electric vehicles. In this framework, each electric vehicle and electric vehicle park is equipped with an intelligent agent. These agents interact with a higher-level agent responsible for managing charging requests within their geographical area. The importance of this multi-agent framework is demonstrated in a case study, where electric vehicles are routed to the most suitable electric vehicle charging parks in their vicinity in a manner that satisfies the charging preferences of customers, with the lowest cost possible, while ensuring that the generation-load balance at the EV parks is not violated.

关键词： Connected Vehicles DDS Electric Vehicles Internet of Electric Vehicles Multi-agent System Routing Management

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Hearing loss prevalence and years lived with disability, 1990-2019: Findings from the Global Burden of Disease Study 2019

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The Lancet 2021年第10278期397卷 996-1009页

作者： Haile, Lydia M. Kamenov, Kaloyan Briant, Paul Svitil Orji, Aislyn U. Steinmetz, Jaimie D. Abdoli, Amir Abdollahi, Mohammad Abu-Gharbieh, Eman Afshin, Ashkan Ahmed, Haroon Rashid, Tarik Ahmed Akalu, Yonas Alahdab, Fares Alanezi, Fahad Mashhour Alanzi, Turki M. Al Hamad, Hanadi Ali, Liaqat Alipour, Vahid Al-Raddadi, Rajaa M. Amu, Hubert Arabloo, Jalal Arab-Zozani, Morteza Arulappan, Judie Ashbaugh, Charlie Atnafu, Desta Debalkie Babar, Zaheer-Ud-Din Baig, Atif Amin Banik, Palash Chandra Bärnighausen, Till Winfried Barrow, Amadou Bender, Rose G. Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bibi, Sadia Bijani, Ali Burkart, Katrin Cederroth, Christopher R. Charan, Jaykaran Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Dagnew, Amare Belachew Dagnew, Baye Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Desalew, Assefa Dhamnetiya, Deepak Dhimal, Mandira Lamichhane Dhimal, Meghnath Doyle, Kerrie E. Duncan, Bruce B. Ekholuenetale, Michael Filip, Irina Fischer, Florian Franklin, Richard Charles Gaidhane, Abhay Motiramji Gaidhane, Shilpa Gallus, Silvano Ghamari, Farhad Ghashghaee, Ahmad Ghozali, Ghozali Gilani, Syed Amir Glǎvan, Ionela-Roxana Golechha, Mahaveer Goulart, Barbara Niegia Garcia Gupta, Veer Bala Gupta, Vivek Kumar Hamidi, Samer Hammond, Billy Randall Hay, Simon I. Hayat, Khezar Heidari, Golnaz Hoffman, Howard J. Hopf, Kathleen Pillsbury Hosseinzadeh, Mehdi Househ, Mowafa Hussain, Rabia Hwang, Bing-Fang Iavicoli, Ivo Ibitoye, Segun Emmanuel Ilesanmi, Olayinka Stephen Irvani, Seyed Sina Naghibi Islam, Sheikh Mohammed Shariful Iwagami, Masao Jacob, Louis Jayapal, Sathish Kumar Jha, Ravi Prakash Jonas, Jost B. Kalhor, Rohollah Al-Salihi, Nawzad Kameran Kandel, Himal Kasa, Ayele Semachew Kayode, Gbenga A. Khalilov, Rovshan Khan, Ejaz Ahmad Khatib, Mahalaqua Nazli Kosen, Soewarta Koyanagi, Ai Kumar, G. Anil Landires, Ivan Lasrado, Savita Lim, Stephen S. Liu, Xuefeng Lobo, Stany W. Lugo, Alessandra Makki, Alaa Mendoza, Walter Mersha, Amanual Getnet Mihretie, Kebadnew Mulatu Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Noncommunicable Diseases World Health Organization (WHO) Geneva Switzerland Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran The Institute of Pharmaceutical Sciences (TIPS) Tehran University of Medical Sciences Tehran Iran School of Pharmacy Tehran University of Medical Sciences Tehran Iran Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Department of Biosciences COMSATS Institute of Information Technology Islamabad Pakistan Department of Computer Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Medical Physiology University of Gondar Gondar Ethiopia Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Health Information Management and Technology Department Dammam Saudi Arabia Geriatric and Long Term Care Department Hamad Medical Corporation Doha Qatar Rumailah Hospital Hamad Medical Corporation Doha Qatar Department of Biological Sciences National University of Medical Sciences (NUMS) Rawalpindi Pakistan Health Management and Economics Research Center Tehran Iran Department of Community Medicine King Abdulaziz University Jeddah Saudi Arabia Department of Population and Behavioural Sciences University of Health and Allied Sciences Ho Ghana Social Determinants of Health Research Center Birjand University of Medical Sciences Birjand Iran Department of Maternal and Child Health Sultan Qaboos University Muscat Oman Department of Health System and Health Economics Bahir Dar University Bahir Dar Ethiopia Department of Pharmacy University of Huddersfield Huddersfield United Kingdom Unit of Biochemistry Universiti

Background: Hearing loss affects access to spoken language, which can affect cognition and development, and can negatively affect social wellbeing. We present updated estimates from the Global Burden of Disease (GBD) study on the prevalence of hearing loss in 2019, as well as the condition's associated disability. Methods: We did systematic reviews of population-representative surveys on hearing loss prevalence from 1990 to 2019. We fitted nested meta-regression models for severity-specific prevalence, accounting for hearing aid coverage, cause, and the presence of tinnitus. We also forecasted the prevalence of hearing loss until 2050. Findings: An estimated 1·57 billion (95% uncertainty interval 1·51–1·64) people globally had hearing loss in 2019, accounting for one in five people (20·3% [19·5–21·1]). Of these, 403·3 million (357·3–449·5) people had hearing loss that was moderate or higher in severity after adjusting for hearing aid use, and 430·4 million (381·7–479·6) without adjustment. The largest number of people with moderate-to-complete hearing loss resided in the Western Pacific region (127·1 million people [112·3–142·6]). Of all people with a hearing impairment, 62·1% (60·2–63·9) were older than 50 years. The Healthcare Access and Quality (HAQ) Index explained 65·8% of the variation in national age-standardised rates of years lived with disability, because countries with a low HAQ Index had higher rates of years lived with disability. By 2050, a projected 2·45 billion (2·35–2·56) people will have hearing loss, a 56·1% (47·3–65·2) increase from 2019, despite stable age-standardised prevalence. Interpretation: As populations age, the number of people with hearing loss will increase. Interventions such as childhood screening, hearing aids, effective management of otitis media and meningitis, and cochlear implants have the potential to ameliorate this burden. Because the burden of moderate-to-complete hearing loss is concentrated in countries with low health-ca

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