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Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge

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arXiv 2021年

作者： Lalande, Alain Chen, Zhihao Pommier, Thibaut Decourselle, Thomas Qayyum, Abdul Salomon, Michel Ginhac, Dominique Skandarani, Youssef Boucher, Arnaud Brahim, Khawla de Bruijne, Marleen Camarasa, Robin Correia, Teresa M. Feng, Xue Girum, Kibrom B. Hennemuth, Anja Huellebrand, Markus Hussain, Raabid Ivantsits, Matthias Ma, Jun Meyer, Craig Sharma, Rishabh Shi, Jixi Tsekos, Nikolaos V. Varela, Marta Wang, Xiyue Yang, Sen Zhang, Hannu Zhang, Yichi Zhou, Yuncheng Zhuang, Xiahai Couturier, Raphael Meriaudeau, Fabrice ImViA laboratory University of Burgundy Dijon France MRI department University Hospital of Dijon Dijon France Femto-ST laboratory University of Franche-Comté Belfort France Cardiology department University Hospital of Dijon Dijon France CASIS Company Quetigny France National Engineering School of Sousse University of Sousse Sousse Tunisia LASEE laboratory National Engineering School of Monastir University of Monastir Monastir Tunisia Biomedical Imaging Group Rotterdam Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Erasmus MC Rotterdam Netherlands Department of Computer Science University of Copenhagen Copenhagen Denmark Centre of Marine Sciences University of Algarve Faro Portugal School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Department of Biomedical Engineering University of Virginia Charlottesville United States Charité – Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany German Centre for Cardiovascular Research Berlin Germany Department of Mathematics Nanjing University of Science and Technology Nanjing China Data Analysis and Intelligent Systems Lab Department of Computer Science University of Houston Houston United States Medical Robotics and Imaging Lab Department of Computer Science University of Houston Houston United States National Heart and Lung Institute Imperial College London London United Kingdom College of Computer Science Sichuan University Chengdu China College of Biomedical Engineering Sichuan University Chengdu China School of Biological Science and Medical Engineering Beihang University Beijing China School of Data Science Fudan University Shanghai China

A key factor for assessing the state of the heart after myocardial infarction (MI) is to measure whether the myocardium segment is viable after reperfusion or revascularization therapy. Delayed enhancement-MRI or DE-MRI, which is performed several minutes after injection of the contrast agent, provides high contrast between viable and nonviable myocardium and is therefore a method of choice to evaluate the extent of MI. To automatically assess myocardial status, the results of the EMIDEC challenge that focused on this task are presented in this paper. The challenge’s main objectives were twofold. First, to evaluate if deep learning methods can distinguish between normal and pathological cases. Second, to automatically calculate the extent of myocardial infarction. The publicly available database consists of 150 exams divided into 50 cases with normal MRI after injection of a contrast agent and 100 cases with myocardial infarction (and then with a hyperenhanced area on DE-MRI), whatever their inclusion in the cardiac emergency department. Along with MRI, clinical characteristics are also provided. The obtained results issued from several works show that the automatic classification of an exam is a reachable task (the best method providing an accuracy of 0.92), and the automatic segmentation of the myocardium is possible. However, the segmentation of the diseased area needs to be improved, mainly due to the small size of these areas and the lack of contrast with the surrounding structures. © 2021, CC BY-NC-SA.

关键词： Cardiology

Mercapto-functionalized scaffold improves perovskite buried interfaces for tandem photovoltaics

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Nature communications 2025年第1期16卷 4917页

作者： Jianan Wang Shuaifeng Hu He Zhu Sanwan Liu Zhongyong Zhang Rui Chen Junke Wang Chenyang Shi Jiaqi Zhang Wentao Liu Xia Lei Bin Liu Yongyan Pan Fumeng Ren Hasan Raza Qisen Zhou Sibo Li Longbin Qiu Guanhaojie Zheng Xiaojun Qin Zhiguo Zhao Shuang Yang Neng Li Jingbai Li Atsushi Wakamiya Zonghao Liu Henry J Snaith Wei Chen Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China. Optics Valley Laboratory Hubei China. Clarendon Laboratory Department of Physics University of Oxford Oxford UK. Institute for Chemical Research Kyoto University Kyoto Japan. State Key Laboratory of Silicate Materials for Architectures Wuhan University of Technology Wuhan China. Department of Materials Science and Engineering Southern University of Science and Technology Shenzhen China. Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Department of Mechanical and Energy Engineering SUSTech Energy Institute for Carbon Neutrality Southern University of Science and Technology Shenzhen China. Shanghai Synchrotron Radiation Facility (SSRF) Zhangjiang Lab Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai China. Huaneng Clean Energy Research Institute Beijing China. School of Materials Science and Engineering East China University of Science and Technology Shanghai China. Hoffmann Institute of Advanced Materials Shenzhen Polytechnic University Shenzhen China. Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China. liuzonghao@***. Optics Valley Laboratory Hubei China. liuzonghao@***. Clarendon Laboratory Department of Physics University of Oxford Oxford UK. henry.snaith@physics.ox.ac.uk. Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China. wnlochenwei@***. Optics Valley Laboratory Hubei China. wnlochenwei@***.

Tandem photovoltaics hold great potential to surpass the efficiency limit of single-junction solar cells. Detrimental structural defects and chemical reactions at buried interfaces of subcells considerably impede the performance of integrated tandems. Here, we devise a mercapto-functionalized mesoporous silica layer as a superstructure at the buried interface to modulate the crystallisation, eliminate nanovoids, passivate defects, and suppress the oxidation of Sn(II) in the tin-lead perovskite films, contributing substantially to reduce charge carrier losses and improve stability in positive-intrinsic-negative structured devices. Consequently, the tin-lead perovskite single-junction cells show efficiency values of up to 23.7% with the best open-circuit voltage of 0.89 V. With the enhanced subcells, our double-junction tandems show efficiency values of 29.6% (certified 29.5% and steady-state 28.7%) and 24.7% on solar cells and 11.3 cm mini-modules, respectively. Encapsulated tandems maintain 90% of initial efficiency after 445 h of maximum power point tracking under simulated 1-sun illumination.

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The GEM benchmark: Natural language generation, its evaluation and metrics

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arXiv 2021年

作者： Gehrmann, Sebastian Adewumi, Tosin Aggarwal, Karmanya Ammanamanchi, Pawan Sasanka Anuoluwapo, Aremu Bosselut, Antoine Chandu, Khyathi Raghavi Clinciu, Miruna Das, Dipanjan Dhole, Kaustubh D. Du, Wanyu Durmus, Esin Dušek, Ondřej Emezue, Chris Gangal, Varun Garbacea, Cristina Hashimoto, Tatsunori Hou, Yufang Jernite, Yacine Jhamtani, Harsh Ji, Yangfeng Jolly, Shailza Kale, Mihir Kumar, Dhruv Ladhak, Faisal Madaan, Aman Maddela, Mounica Mahajan, Khyati Mahamood, Saad Majumder, Bodhisattwa Prasad Martins, Pedro Henrique McMillan-Major, Angelina Mille, Simon Van Miltenburg, Emiel Nadeem, Moin Narayan, Shashi Nikolaev, Vitaly Niyongabo, Rubungo Andre Osei, Salomey Parikh, Ankur Perez-Beltrachini, Laura Rao, Niranjan Ramesh Raunak, Vikas Rodriguez, Juan Diego Santhanam, Sashank Sedoc, João Sellam, Thibault Shaikh, Samira Shimorina, Anastasia Sobrevilla Cabezudo, Marco Antonio Strobelt, Hendrik Subramani, Nishant Xu, Wei Yang, Diyi Yerukola, Akhila Zhou, Jiawei Amelia R&D New York United States Carnegie Mellon University United States Charles University Prague Czech Republic Columbia University United States Cornell University United States DFKI Germany Edinburgh Centre for Robotics United Kingdom Georgia Tech United States Google Research United States Harvard University United States Heriot-Watt University United Kingdom Hugging Face United States IBM Research IIIT Delhi India IIIT Hyderabad India Instituto de Telecomunicações Portugal Intelligent Systems Lab. Intel Kwame Nkrumah University of Science and Technology Ghana Luleå University of Technology Sweden Masakhane South Africa Massachusetts Institute of Technology United States Microsoft United States National Institute of Technology Karnataka India New York University United States Pompeu Fabra University Spain Samsung Research Stanford University United States Technical University of Kaiserslautern Germany Technical University Munich Germany Tilburg University Netherlands trivago Germany Université de Lorraine France University of North Carolina Charlotte United States University of Edinburgh United Kingdom University of Electronic Science and Technology of China China University of California San Diego United States University of Lagos Nigeria University of Michigan Ann Arbor United States University of São Paulo Brazil University of Texas Austin United States University of Virginia United States University of Washington United States University of Waterloo Canada

We introduce GEM, a living benchmark for natural language Generation (NLG), its Evaluation, and Metrics. Measuring progress in NLG relies on a constantly evolving ecosystem of automated metrics, datasets, and human evaluation standards. Due to this moving target, new models often still evaluate on divergent anglo-centric corpora with well-established, but flawed, metrics. This disconnect makes it challenging to identify the limitations of current models and opportunities for progress. Addressing this limitation, GEM provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested. Regular updates to the benchmark will help NLG research become more multilingual and evolve the challenge alongside models. This paper serves as the description of the data for which we are organizing a shared task at our ACL 2021 Workshop and to which we invite the entire NLG community to participate. © 2021, CC BY.

关键词： Natural language processing systems

Metrics Reloaded: Recommendations for image analysis validation

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation

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arXiv 2017年

作者： Giftthaler, Markus Neunert, Michael Stäuble, Markus Frigerio, Marco Semini, Claudio Buchli, Jonas Agile & Dexterous Robotics Lab Institute of Robotics and Intelligent Systems ZürichETH Switzerland Dept. of Advanced Robotics Istituto Italiano di Tecnologia Genova Italy

Many algorithms for control, optimization and estimation in robotics depend on derivatives of the underlying system dynamics, e.g. to compute linearizations, sensitivities or gradient directions. However, we show that when dealing with Rigid Body Dynamics, these derivatives are difficult to derive analytically and to implement efficiently. To overcome this issue, we extend the modelling tool ‘RobCoGen’ to be compatible with Automatic Differentiation. Additionally, we propose how to automatically obtain the derivatives and generate highly efficient source code. We highlight the flexibility and performance of the approach in two application examples. First, we show a Trajectory Optimization example for the quadrupedal robot HyQ, which employs auto-differentiation on the dynamics including a contact model. Second, we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly moving obstacle in a go-to task by fast, dynamic replanning. This paper is an extended version of [1]. Copyright © 2017, The Authors. All rights reserved.

关键词： Aerodynamics

AFA-PredNet: The action modulation within predictive coding

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arXiv 2018年

作者： Zhong, Junpei Cangelosi, Angelo Zhang, Xinzheng Ogata, Tetsuya Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan Centre for Robotics and Neural Systems Plymouth University PlymouthPL4 8AA United Kingdom School of Electrical Engineering Jinan University Zhuhai China Lab for Intelligent Dynamics and Representation Waseda University Tokyo Japan

— The predictive processing (PP) hypothesizes that the predictive inference of our sensorimotor system is encoded implicitly in the regularities between perception and action. We propose a neural architecture in which such regularities of active inference are encoded hierarchically. We further suggest that this encoding emerges during the embodied learning process when the appropriate action is selected to minimize the prediction error in perception. Therefore, this predictive stream in the sensorimotor loop is generated in a top-down manner. Specifically, it is constantly modulated by the motor actions and is updated by the bottom-up prediction error signals. In this way, the top-down prediction originally comes from the prior experience from both perception and action representing the higher levels of this hierarchical cognition. In our proposed embodied model, we extend the PredNet Network, a hierarchical predictive coding network, with the motor action units implemented by a multi-layer perceptron network (MLP) to modulate the network top-down prediction. Two experiments, a minimalistic world experiment, and a mobile robot experiment are conducted to evaluate the proposed model in a qualitative way. In the neural representation, it can be observed that the causal inference of predictive percept from motor actions can be also observed while the agent is interacting with the environment. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

Research on Position Sensorless Control of PMSM Based on Improved SMO

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IOP Conference Series: Materials Science and Engineering 2019年第1期533卷

作者： Zhang Guoqing Xiong Xinhong Zheng Yousheng Zhang Yanhui Chen Lianbo Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China

The sliding mode observer (SMO) can effectively achieve the permanent magnet synchronous motor (PMSM) to perform position sensorless control, but there are still drawbacks of system chattering and high complexity. In this paper, an improved SMO is established based on hyperbolic tangent function and Kalman filter. The system chattering is effectively suppressed, and the accuracy of position and speed observation error is significantly improved without phase compensation. It is found that the convergence rate of the hyperbolic tangent function has a strong correlation with the system chattering degree and the speed observation error. When a is between 3 and 4, the speed error accuracy of the improved SMO is the optimum situation.

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Parameter Tuning of PID Controller of Servo System Based on Particle Swarm Optimization Algorithm

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IOP Conference Series: Materials Science and Engineering 2019年第1期533卷

作者： Chen Lianbo Xiong Xinhong Zheng Yousheng Zhang Yanhui Zhang Guoqing Wu Xinyu Feng Wei Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China CAS Key Laboratory of Human-Machine-Intelligence Synergic Systems Shenzhen Institutes of Advanced Technology Shenzhen 518055 China School of Logistics Engineering Wuhan University of Technology Wuhan 430070 China

In order to improve the control precision of permanent magnet synchronous motor servo drive, a Proportional-Integral-Differential (PID) control method based on particle swarm optimization (PSO) algorithm is proposed. Firstly, the mathematical model of the permanent magnet synchronous motor was established. Then the PSO algorithm was used to adjust the parameters of the PID controller. Finally, the simulation experiment was carried out under the MATlab/Simulink environment to test its performance. Compared with the traditional manual parameters tuning, the PID controller based on PSO algorithm had better performance and better stability.

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Magnetoelectric Catalysis: Magnetoelectrically Driven Catalytic Degradation of Organics (Adv. Mater. 28/2019)

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Advanced Materials 2019年第28期31卷

作者： Fajer Mushtaq Xiangzhong Chen Harun Torlakcik Christian Steuer Marcus Hoop Erdem Can Siringil Xavi Marti Gregory Limburg Patrick Stipp Bradley J. Nelson Salvador Pané Multi‐Scale Robotics Lab (MSRL) Institute of Robotics and Intelligent Systems (IRIS) ETH Zurich CH‐8092 Zurich Switzerland Institute of Pharmaceutical Sciences ETH Zurich CH‐8093 Zurich Switzerland Institute of Physics Academy of Sciences of the Czech Republic Cukrovarnická 10 16200 Praha 6 Czech Republic IGS Research Ltd. Calle La Coma Nave 8 43140 La Pobla de Mafumet Tarragona Spain