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Deep learning for LiDAR point clouds in autonomous driving: A review

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arXiv 2020年

作者： Li, Ying Ma, Lingfei Zhong, Zilong Liu, Fei Cao, Dongpu Li, Jonathan Chapman, Michael A. Department of Geography and Environmental Management University of Waterloo 200 University Avenue West WaterlooN2L 3G1 Canada School of Data and Computer Science Sun Yat-Sen University Guangzhou510006 China Xilinx Technology Beijing Limited Beijing100083 China Waterloo Cognitive Autonomous Driving Lab University of Waterloo N2L 3G1 Canada Departments of System Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Civil Engineering Ryerson University TorontoONM5B 2K3 Canada

Recently, the advancement of deep learning in discriminative feature learning from 3D LiDAR data has led to rapid development in the field of autonomous driving. However, automated processing uneven, unstructured, noisy, and massive 3D point clouds is a challenging and tedious task. In this paper, we provide a systematic review of existing compelling deep learning architectures applied in LiDAR point clouds, detailing for specific tasks in autonomous driving such as segmentation, detection, and classification. Although several published research papers focus on specific topics in computer vision for autonomous vehicles, to date, no general survey on deep learning applied in LiDAR point clouds for autonomous vehicles exists. Thus, the goal of this paper is to narrow the gap in this topic. More than 140 key contributions in the recent five years are summarized in this survey, including the milestone 3D deep architectures, the remarkable deep learning applications in 3D semantic segmentation, object detection, and classification;specific datasets, evaluation metrics, and the state of the art performance. Finally, we conclude the remaining challenges and future researches. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

Adversarial machine learning

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Synthesis Lectures on Artificial Intelligence and Machine Learning 2018年第3期12卷 1-169页

作者： Vorobeychik, Yevgeniy Kantarcioglu, Murat Vanderbilt University Department of Computer Science Computer Engineering and Biomedical Informatics United States UTD Data Security and Privacy Lab. University of Texas Dallas United States

ISBN: (纸本)9781681733951

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at causing congestion or accidents, or may even model unusual situations that expose vulnerabilities in the prediction engine. Other applications are adversarial because their task and/or the data they use are. For example, an important class of problems in security involves detection, such as malware, spam, and intrusion detection. The use of machine learning for detecting malicious entities creates an incentive among adversaries to evade detection by changing their behavior or the content of malicius objects they develop. The field of adversarial machine learning has emerged to study vulnerabilities of machine learning approaches in adversarial settings and to develop techniques to make learning robust to adversarial manipulation. This book provides a technical overview of this field. After reviewing machine learning concepts and approaches, as well as common use cases of these in adversarial settings, we present a general categorization of attacks on machine learning. We then address two major categories of attacks and associated defenses: decision-time attacks, in which an adversary changes the nature of instances seen by a learned model at the time of prediction in order to cause errors, and poisoning or training time attacks, in which the actual training dataset is maliciously modified. In our final chapter devoted to technical content, we discuss recent techniques for attacks on deep learning, as well as approaches for

关键词： Game theory

Structured query construction via knowledge graph embedding

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arXiv 2019年

作者： Wang, Ruijie Wang, Meng Liu, Jun Cochez, Michael Decker, Stefan National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an China School of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China School of Computer Science and Engineering Southeast University Nanjing China Guang Dong Xi'an Jiaotong University Academy Shunde China VU Amsterdam Amsterdam Netherlands Fraunhofer FIT Sankt Augustin Germany Informatik 5 RWTH Aachen University Aachen Germany Faculty of Information Technology University of Jyvskyl Jyvskyl Finland

In order to facilitate the accesses of general users to knowledge graphs, an increasing effort is being exerted to construct graph-structured queries of given natural language questions. At the core of the construction is to deduce the structure of the target query and determine the vertices/edges which constitute the query. Existing query construction methods rely on question understanding and conventional graph-based algorithms which lead to inefficient and degraded performances facing complex natural language questions over knowledge graphs with large scales. In this paper, we focus on this problem and propose a novel framework standing on recent knowledge graph embedding techniques. Our framework first encodes the underlying knowledge graph into a low-dimensional embedding space by leveraging generalized local knowledge graphs. Given a natural language question, the learned embedding representations of the knowledge graph are utilized to compute the query structure and assemble vertices/edges into the target query. Extensive experiments were conducted on the benchmark dataset, and the results demonstrate that our framework outperforms state-of-the-art baseline models regarding effectiveness and efficiency. Copyright © 2019, The Authors. All rights reserved.

关键词： Knowledge graph

PLUMED Tutorials: a collaborative, community-driven learning ecosystem

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arXiv 2024年

作者： Tribello, Gareth A. Bonomi, Massimiliano Bussi, Giovanni Camilloni, Carlo Armstrong, Blake I. Arsiccio, Andrea Aureli, Simone Ballabio, Federico Bernetti, Mattia Bonati, Luigi Brookes, Samuel G.H. Faidon Brotzakis, Z. Capelli, Riccardo Ceriotti, Michele Chan, Kam-Tung Cossio, Pilar Dasetty, Siva Donadio, Davide Ensing, Bernd Ferguson, Andrew L. Fraux, Guillaume Gale, Julian D. Gervasio, Francesco Luigi Giorgino, Toni Herringer, Nicholas S.M. Hocky, Glen M. Hoff, Samuel E. Invernizzi, Michele Languin-Cattoën, Olivier Leone, Vanessa Limongelli, Vittorio Lopez-Acevedo, Olga Marinelli, Fabrizio Martinez, Pedro Febrer Masetti, Matteo Mehdi, Shams Michaelides, Angelos Murtada, Mhd Hussein Parrinello, Michele Piaggi, Pablo M. Pietropaolo, Adriana Pietrucci, Fabio Pipolo, Silvio Pritchard, Claire Raiteri, Paolo Raniolo, Stefano Rapetti, Daniele Rizzi, Valerio Rydzewski, Jakub Salvalaglio, Matteo Schran, Christoph Seal, Aniruddha Zadeh, Armin Shayesteh Silva, Tomás F.D. Spiwok, Vojtěch Stirnemann, Guillaume Sucerquia, Daniel Tiwary, Pratyush Valsson, Omar Vendruscolo, Michele Voth, Gregory A. White, Andrew D. Wu, Jiangbo Centre for Quantum Materials and Technology School of Mathematics and Physics Queen’s University Belfast United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Via Bonomea 265 Trieste34136 Italy Department of Biosciences University of Milano Milano Italy School of Molecular and Life Sciences Curtin University GPO Box U1987 PerthWA6845 Australia Coriolis Pharma Fraunhoferstrasse 18b Martinsried82152 Germany School of Pharmaceutical Sciences University of Geneva Rue Michel-Servet 1 GenevaCH-1206 Switzerland Institute of Pharmaceutical Sciences of Western Switzerland University of Geneva GenevaCH-1206 Switzerland Swiss Bioinformatics Institute University of Geneva GenevaCH-1206 Switzerland Via Celoria 26 Milan20133 Italy Department of Biomolecular Sciences University of Urbino "Carlo Bo" Piazza Rinascimento 6 Urbino61029 Italy Department of Pharmacy and Biotechnology Alma Mater Studiorum University of Bologna Via Belmeloro 6 Bologna40126 Italy Atomistic Simulations Italian Institute of Technology Genova Italy Yusuf Hamied Department of Chemistry University of Cambridge CambridgeCB2 1EW United Kingdom Institute for Bioinnovation Biomedical Sciences Research Center "Alexander Fleming" Vari16672 Greece Laboratory of Computational Science and Modeling Institut des Matériaux École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Chemistry University of California Davis DavisCA95616 United States Center for Computational Mathematics Flatiron Institute New YorkNY10010 United States Center for Computational Biology Flatiron Institute New YorkNY10010 United States Pritzker School of Molecular Engineering University of Chicago ChicagoIL60637 United States AI4Science Lab Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands Computational Chemistry Group Van’t Hoff Institute for Molecular Sciences Unive

In computational physics, chemistry, and biology, the implementation of new techniques in a shared and open source software lowers barriers to entry and promotes rapid scientific progress. However, effectively training new software users presents several challenges. Common methods like direct knowledge transfer and in-person workshops are limited in reach and comprehensiveness. Furthermore, while the COVID-19 pandemic highlighted the benefits of online training, traditional online tutorials can quickly become outdated and may not cover all the software’s functionalities. To address these issues, here we introduce "PLUMED Tutorials", a collaborative model for developing, sharing, and updating online tutorials. This initiative utilizes repository management and continuous integration to ensure compatibility with software updates. Moreover, the tutorials are interconnected to form a structured learning path and are enriched with automatic annotations to provide broader context. This paper illustrates the development, features, and advantages of PLUMED Tutorials, aiming to foster an open community for creating and sharing educational resources. © 2024, CC BY.

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Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder

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Aberrant Functional Network Connectivity Transition Probabil...

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Elaheh Zendehrouh Mohammad. S. E. Sendi Jing Sui Zening Fu Dongmei Zhi Luxian Lv Xiaohong Ma Qing Ke Xianbin Li Chuanyue Wang Christopher. C. Abbott Jessica A. Turner Robyn. L Miller Vince D. Calhoun Department of Computer Science Georgia State University Atlanta Georgia Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta Georgia Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Brainnetome Center and National Laboratory of Pattern Recognition Beijing China and University of Chinese Academy of Sciences Beijing China Psychiatric Laboratory and Mental Health Center the State Key Laboratory of Biotherapy West China Hospital of Sichuan University Chengdu China Huaxi Brain Research Center West China Hospital of Sichuan University Chengdu China Beijing Key Lab of Mental Disorders Capital Medical University Beijing China Department of Psychiatry University of New Mexico Albuquerque New Mexico

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.

关键词： Hidden Markov models Hospitals Functional magnetic resonance imaging Correlation Probability Microsoft Windows Psychiatry

Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

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arXiv 2020年

作者： Chatterjee, Soumick Saad, Fatima Sarasaen, Chompunuch Ghosh, Suhita Krug, Valerie Khatun, Rupali Mishra, Rahul Desai, Nirja Radeva, Petia Rose, Georg Stober, Sebastian Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Italy Institute for Medical Engineering Otto von Guericke University Magdeburg Germany Research Campus STIMULATE Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany Artificial Intelligence Lab Otto von Guericke University Magdeburg Germany Department of Mathematics and Computer Science University of Barcelona Barcelona Spain Translational Radiobiology Department of Radiation Oncology Universitätsklinikum Erlangen Erlangen Germany Apollo Hospitals Bilaspur India HCG Cancer Centre Vadodara India Computer Vision Centre Cerdanyola Barcelona Spain Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany

The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosing infected patients. Medical imaging, such as X-ray and Computed Tomography (CT), combined with the potential of Artificial Intelligence (AI), plays an essential role in supporting medical personnel in the diagnosis process. Thus, in this article five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2 and DenseNet161) and their ensemble, using majority voting have been used to classify COVID-19, pneumoniæ and healthy subjects using chest X-ray images. Multilabel classification was performed to predict multiple pathologies for each patient, if present. Firstly, the interpretability of each of the networks was thoroughly studied using local interpretability methods - occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT, and using a global technique - neuron activation profiles. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the ensemble of the network models. The qualitative results showed that the ResNets were the most interpretable models. This research demonstrates the importance of using interpretability methods to compare different models before making a decision regarding the best performing model. Copyright © 2020, The Authors. All rights reserved.

关键词： COVID-19

Roadmap for Optical Tweezers

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arXiv 2022年

作者： Volpe, Giovanni Maragò, Onofrio M. Rubinzstein-Dunlop, Halina Pesce, Giuseppe Stilgoe, Alexander B. Volpe, Giorgio Tkachenko, Georgiy Truong, Viet Giang Chormaic, Síle Nic Kalantarifard, Fatemeh Elahi, Parviz Käll, Mikael Callegari, Agnese Marqués, Manuel I. Neves, Antonio A.R. Moreira, Wendel L. Fontes, Adriana Cesar, Carlos L. Saija, Rosalba Saidi, Abir Beck, Paul Eismann, Jörg S. Banzer, Peter Fernandes, Thales F.D. Pedaci, Francesco Bowen, Warwick P. Vaippully, Rahul Lokesh, Muruga Roy, Basudev Thalhammer, Gregor Ritsch-Marte, Monika García, Laura Pérez Arzola, Alejandro V. Castillo, Isaac Pérez Argun, Aykut Muenker, Till M. Vos, Bart E. Betz, Timo Cristiani, Ilaria Minzioni, Paolo Reece, Peter J. Wang, Fan McGloin, David Ndukaife, Justus C. Quidant, Romain Roberts, Reece P. Laplane, Cyril Volz, Thomas Gordon, Reuven Hanstorp, Dag Marmolejo, Javier Tello Bruce, Graham D. Dholakia, Kishan Li, Tongcang Brzobohatý, Oto Simpson, Stephen H. Zemánek, Pavel Ritort, Felix Roichman, Yael Bobkova, Valeriia Wittkowski, Raphael Denz, Cornelia Kumar, G.V. Pavan Foti, Antonino Donato, Maria Grazia Gucciardi, Pietro G. Gardini, L. Bianchi, G. Kashchuk, A. Capitanio, M. Paterson, Lynn Jones, P.H. Berg-Sørensen, Kirstine Barooji, Younes F. Oddershede, Lene B. Pouladian, Pegah Preece, Daryl Adiels, Caroline Beck De Luca, Anna Chiara Magazzù, A. Ciriza, D. Bronte Iatì, M.A. Swartzlander, Grover A. Department of Physics University of Gothenburg Gothenburg41296 Sweden CNR IPCF Istituto per i Processi Chimico-Fisici Messina Italy ARC CoE for Engineered Quantum Systems School of Mathematics and Physics The University of Queensland Australia Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom Dipartimento di Fisica Università degli studi di Napoli Federico II Italy Okinawa Institute of Science and Technology Graduate University Japan Department of Health Technology Technical University of Denmark Lyngby Denmark Physics Department Boğaziçi University Istanbul Turkey Chalmers University of Technology Gothenburg Sweden Departamento de Física de Materiales IFIMAC Instituto Nicolás Cabrera Universidad Autónoma de Madrid Spain Santo André São Paulo Brazil Tratamento de dados geofísicos Tecnologia e processamento de geologia e geofísica Exploração Petrobras - Petróleo Brasileiro S.A. Rio de Janeiro Rio de Janeiro Brazil Pernambuco Recife Brazil Ceará Fortaleza Brazil Campinas São Paulo Brazil Dipartimento di Scienze Matematiche e Informatiche Scienze Fisiche e Scienze della Terra Università di Messina Italy Institute of Optics Information and Photonics University Erlangen-Nuremberg Staudtstr. 7/B2 ErlangenD-91058 Germany Max Planck Institute for the Science of Light Staudtstr. 2 ErlangenD-91058 Germany Institute of Physics University of Graz NAWI Graz Universitätsplatz 5 Graz8010 Austria Centre de Biologie Structurale CNRS INSERM Univ.Montpellier France Indian Institute of Technology Madras India Institute for Biomedical Physics Medical University of Innsbruck Austria Instituto de Física Universidad Nacional Autónoma de México Ciudad de MéxicoC. P. 04510 Mexico Departamento de Física Universidad Autónoma Metropolitana-Iztapalapa San Rafael Atlixco 186 Ciudad de México09340 Mexico Third Institute of Physics - Biophysics Georg August University Göttingen Germany Department of Electrical

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical tweezers

Biomedical image analysis competitions: The state of current participation practice

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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

The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future (vol 398, pg 1619, 2021)

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LANCET 2021年第10317期398卷 2148-2148页

作者： Romanello, M. McGushin, A. Di Napoli, C. [a]Institute for Global Health University College London London UK [b]Institute for Sustainable Resources University College London London UK [c]UCL Energy Institute University College London London UK [d]Institute for Environmental Design and Engineering University College London London UK [e]Department of Geography University College London London UK [f]The Bartlett School of Sustainable Construction University College London London UK [g]Centre for Human Health and Performance University College London London UK [h]School of Agriculture Policy and Development University of Reading Reading UK [i]Department of Meteorology University of Reading Reading UK [j]The Walker Institute University of Reading Reading UK [k]Department of Health Sciences University of York York UK [l]Institute for Environment and Human Security United Nations University Bonn Germany [m]Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK [n]Centre for Mathematical Modelling of Infectious Diseases London School of Hygiene & Tropical Medicine London UK [o]Department of Public Health Environments and Society London School of Hygiene & Tropical Medicine London UK [p]Department of Earth System Science Tsinghua University Beijing China [q]Department of Environment Climate Change and Health World Health Organization Geneva Switzerland [r]Institute for Environmental Sciences World Health Organization Geneva Switzerland [s]Centre for Climate Change and Social Transformations School of Psychology Cardiff University Cardiff UK [t]Yale Center on Climate Change and Health Yale University New Haven CT USA [u]School of Government University of Birmingham Birmingham UK [v]Economic analysis of Climate Impacts and Policy Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy [w]Natural Resources Institute University of Greenwich London UK [x]Department of Global Health University of Washington Seattle WA USA [y]Centre for He