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AIROGS: ARTIFICIAL INTELLIGENCE FOR ROBUST GLAUCOMA SCREENING CHALLENGE

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arXiv 2023年

作者： De Vente, Coen Vermeer, Koenraad A. Jaccard, Nicolas Wang, He Sun, Hongyi Khader, Firas Truhn, Daniel Aimyshev, Temirgali Zhanibekuly, Yerkebulan Le, Tien-Dung Galdran, Adrian Ballester, Miguel Ángel González Carneiro, Gustavo Devika, R.G. Hrishikesh, P.S. Puthussery, Densen Liu, Hong Yang, Zekang Kondo, Satoshi Kasai, Satoshi Wang, Edward Durvasula, Ashritha Heras, Jónathan Zapata, Miguel Ángel Araújo, Teresa Aresta, Guilherme Bogunović, Hrvoje Arikan, Mustafa Lee, Yeong Chan Cho, Hyun Bin Choi, Yoon Ho Qayyum, Abdul Razzak, Imran Van Ginneken, Bram Lemij, Hans G. Sánchez, Clara I. Group Informatics Institute Universiteit Van Amsterdam Noord-Holland Amsterdam Netherlands The Department of Biomedical Engineering and Physics Amsterdam Umc Locatie Amc Noord-Holland Amsterdam Netherlands Department of Radiology and Nuclear Medicine Radboudumc Gelderland Nijmegen Netherlands The Rotterdam Ophthalmic Institute Rotterdam Eye Hospital Rotterdam Netherlands Project Orbis International Inc. New York United States The Peking Union Medical College Hospital Beijing China The Xuanwu Hospital Capital Medical University Beijing100053 China The Tsinghua University Beijing China The Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Cmc Technologies Llp Nur-Sultan Kazakhstan Kbc Belgium Universitat Pompeu Fabra Barcelona Spain The Australian Institute for Machine Learning Aiml University of Adelaide Australia Icrea Barcelona Spain The Centre for Vision Speech and Signal Processing University of Surrey United Kingdom The College of Engineering Trivandrum India Founding Minds Software India The Institute of Computing Technology Chinese Academy of Sciences China The Muroran Institute of Technology Japan The Niigata University of Health and Welfare Japan The Schulich School of Medicine and Dentistry University of Western Ontario London Canada The Department of Mathematics and Computer Science University of la Rioja Spain Hospital Vall Hebron Passeig Roser 126 Sant Cugat del Vallès Barcelona08195 Spain UPRetina Barcelona Spain The Christian Doppler Laboratory for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria The Institute of Ophthalmology University College London London United Kingdom The Research Institute for Future Medicine Samsung Medical Center Seoul Korea Republic of Sungkyunkwan University Samsung Medical Center Seoul Korea Republic of The Department of Artificial Intelligence and Informatics Mayo Clinic Jacks

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in realworld scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening. Copyright © 2023, The Authors. All rights reserved.

关键词： Diagnosis

Towards self-explainable cyber-physical systems

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arXiv 2019年

作者： Blumreiter, Mathias Greenyer, Joel Garcia, Francisco Javier Chiyah Klös, Verena Schwammberger, Maike Sommer, Christoph Vogelsang, Andreas Wortmannz, Andreas Institute for Software Systems Hamburg University of Technology Software Engineering Group Leibniz Universität Hannover Germany Heriot-Watt University Software and Embedded Systems Engineering Technische Universität Berlin Germany Department of Computing Science University of Oldenburg Heinz Nixdorf Institute Dept. of Computer Science Paderborn University Automated Systems Engineering Technologies Technische Universität Berlin Germany Software Engineering RWTH Aachen University

With the increasing complexity of Cyber-Physical Systems, their behavior and decisions become increasingly difficult to understand and comprehend for users and other stakeholders. Our vision is to build self-explainable systems that can, at run-time, answer questions about the system's past, current, and future behavior. As hitherto no design methodology or reference framework exists for building such systems, we propose the Monitor, Analyze, Build, Explain (MAB-EX) framework for building self-explainable systems that leverage requirements- A nd explainability models at run-time. The basic idea of MAB-EX is to first Monitor and Analyze a certain behavior of a system, then Build an explanation from explanation models and convey this EXplanation in a suitable way to a stakeholder. We also take into account that new explanations can be learned, by updating the explanation models, should new and yet unexplainable behavior be detected by the system. Copyright © 2019, The Authors. All rights reserved.

关键词： Embedded systems

Thermal Loading and Analysis of Modular Multilevel Converters using Injection Control of Circulating Current and Common-mode Voltage

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Thermal Loading and Analysis of Modular Multilevel Converter...

IEEE Energy Conversion Congress and Exposition

作者： Deepak Ronanki Sheldon S. Williamson Electric Mobility and Transportation Innovation (E-MOTION) Laboratory Smart Transportation Electrification and Energy Research (STEER) Group Faculty of Engineering and Applied Science Department of Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa ON Canada

The circulating current (CC) is an undesirable phenomenon in a modular multilevel converter (MMC), as it increases the capacitor voltage ripple, power losses and current stress on power semiconductor devices. Prior research has mainly focused on various control methods as well as modulation schemes to minimize the CC. This paper presents a comprehensive analysis of an MMC under common-mode voltage (CMV) injection and CC control strategies in terms of thermal loading and failure rate of power semiconductor devices. To benchmark these control strategies, a generalized control structure for the MMC is presented. To study the thermal loading on power semiconductor devices, an electro-thermal model is developed. The effectiveness of the presented control approach is demonstrated through detailed simulations and experimental studies using hardware-in-the-loop (HIL) system. Finally, the loading on power semiconductor devices of the MMC is evaluated under injection control of CC and CMV for different modulation schemes.

关键词： Voltage control Capacitors Modulation Power semiconductor devices Loading Stress Load modeling

Artificial intelligence in PET: An industry perspective

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arXiv 2021年

作者： Sitek, Arkadiusz Ahn, Sangtae Asma, Evren Chandler, Adam Ihsani, Alvin Prevrhal, Sven Rahmim, Arman Saboury, Babak Thielemans, Kris Sano Centre for Computational Medicine Nawojki 11 Street Kraków30-072 Poland GE Research Canon Medical Research United Kingdom Global Scientific Collaborations Group United Imaging Healthcare United States NVIDIA United States Philips Research Europe Departments of Radiology and Physics University of British Columbia Provincial Medical Imaging Physicist BC Cancer Canada Department of Radiology and Imaging Sciences Clinical Center National Institutes of Health Department of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Department of Radiology Hospital of the University of Pennsylvania United States Institute of Nuclear Medicine UCL London United Kingdom Algorithms and Software Consulting Ltd London United Kingdom

Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications. AI has the ability to enhance and optimize all aspects of the PET imaging chain from patient scheduling, patient setup, protocoling, data acquisition, detector signal processing, reconstruction, image processing and interpretation. AI poses industry-specific challenges which will need to be addressed and overcome to maximize the future potentials of AI in PET. This paper provides an overview of these industry-specific challenges for the development, standardization, commercialization, and clinical adoption of AI, and explores the potential enhancements to PET imaging brought on by AI in the near future. In particular, the combination of on-demand image reconstruction, AI, and custom designed data processing workflows may open new possibilities for innovation which would positively impact the industry and ultimately patients. Copyright © 2021, The Authors. All rights reserved.

关键词： Positron emission tomography

Cross-Device Radio Map Generation via Crowdsourcing

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Cross-Device Radio Map Generation via Crowdsourcing

International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Georgios Pipelidis Nikolaos Tsiamitros Efdal Ustaoglu Romeo Kienzler Petteri Nurmi Huber Flores Christian Prehofer Ariadne Maps Munich Germany Technical University of Munich Software and Systems Engineering Research Group Munich Germany IBM Watson Internet of Things Zurich Switzerland Department of Computer Science University of Helsinki Helsinki

Crowdsourcing is a powerful technique for bootstrapping sensing systems that are based on wireless signals. For example, wireless sensing systems can ask users to contribute training data and localization systems (such as WiFi fingerprinting) can take advantage of wireless measurements provided by users of the system. Indeed, previous research has demonstrated that crowdsourcing can reduce labor efforts needed to deploy and initialize wireless sensing systems by several orders of magnitude, without compromising on system performance. Despite the many benefits of crowdsourcing, current methods suffer from one significant drawback, namely that they are highly sensitive to variations in devices capturing the measurements. Indeed, as we demonstrate in this paper, cross-device variations can decrease performance of crowdsourced bootstrapping approaches up to 70of radio maps used for localization and to make them robust against cross-device variations in wireless signals. We evaluate our framework by considering WiFi fingerprinting based localization as a representative example of applications that benefit from our approach. Our results demonstrate up to 1.8m localization error, and 18.7.

关键词： Wireless communication Wireless sensor networks Elbow Indoor navigation Crowdsourcing Sensors Wireless fidelity

Quantum speedup for track reconstruction in particle accelerators

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Physical Review D 2022年第7期105卷 076012-076012页

作者： D. Magano A. Kumar M. Kālis A. Locāns A. Glos S. Pratapsi G. Quinta M. Dimitrijevs A. Rivošs P. Bargassa J. Seixas A. Ambainis Y. Omar Physics of Information and Quantum Technologies Group Instituto de Telecomunicações Av. Rovisco Pais 1 1049-001 Lisboa Portugal Instituto Superior Técnico Universidade de Lisboa Av. Rovisco Pais 1 1049-001 Lisboa Portugal Department of Mathematics Clarkson University Potsdam New York 13699 USA Center for Quantum Computer Science Faculty of Computing University of Latvia Raiņa bulv. 19 R𝚤ga LV-1586 Latvia Institute of Theoretical and Applied Informatics Polish Academy of Sciences Bałtycka 5 44-100 Gliwice Poland Portuguese Quantum Institute Av. Rovisco Pais 1 1049-001 Lisboa Portugal Laboratório de Instrumentação e Física Experimental de Partículas Av. Pr. Gama Pinto 2 1649-003 Lisbon Portugal Center for Physics and Engineering of Advanced Materials Av. Rovisco Pais 1 1049-001 Lisbon Portugal

To investigate the fundamental nature of matter and its interactions, particles are accelerated to very high energies and collided inside detectors, producing a multitude of other particles that are scattered in all directions. As charged particles traverse the detector, they leave signals of their passage. The problem of track reconstruction is to recover the original trajectories from these signals. This challenging data analysis task will become even more demanding as the luminosity of future accelerators increases, leading to collision events with a more complex structure. We identify four fundamental routines present in every local tracking method and analyze how they scale in the context of a standard tracking algorithm. We show that for some of these routines we can reach a lower computational complexity with quantum search algorithms. Although the found quantum speedups are mild, this constitutes, to the best of our knowledge, the first rigorous evidence of a quantum advantage for a high-energy physics data processing task.

关键词： Quantum algorithms

Introduction of Fog Computations to Measuring System Collection and Accounting of Data of Distributed Internet of Things

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： V D Munister A L Zolkin T G Aygumov V E Ozhiganov Mariupol State University Department of Enterprise Economics State educational institution of higher professional education "Donetsk National Technical University" Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Department of computer engineering software and automated systems Dagestan State Technical University Federal State Unitary Enterprise RUSSIAN FEDERAL NUCLEAR CENTER All-Russian Research Institute of Experimental Physics FSUE RFNC – VNIIEF

The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device's behaviour at a given time *** practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given.

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An improved ORNAM representation of grey images

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An improved ORNAM representation of grey images

作者： Zheng, Yunping Sarem, Mudar School of Computer Science and Engineering South China University of Technology Guangzhou China Medical Image Processing Group Department of Radiology University of Pennsylvania Philadelphia United States School of Software Engineering Huazhong University of Science and Technology Wuhan China

An efficient image representation can save space and facilitate the manipulation of the acquired images. In order to further enhance the reconstructed image quality and reduce the number of the homogeneous blocks of the overlapping rectangular non-symmetry and anti-packing model (ORNAM) representation, in this paper, we propose an improved overlapping rectangular non-symmetry and anti-packing model representation (IORNAM) of grey images. Compared with most of the up-to-date and the state-of-the-art hierarchical representation methods, the new IORNAM representation is characterised by two properties: 1) it adopts a ratio parameter of the length and the width of a homogenous block to improve the reconstructed image quality;2) it uses a new expansion method to anti-pack the subpatterns of grey images to further decrease the number of homogenous blocks, which is important for improving the compression ratios of image representation and reducing the complexities of many image manipulation algorithms. The experimental results presented in this paper demonstrate that: 1) the new IORNAM representation is able to achieve high representation efficiency for grey images;2) the new IORNAM representation outperforms most of the up-to-date and the state-of-the-art hierarchical representation methods of grey images. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Image quality

Fine-grained event categorization with heterogeneous graph convolutional networks

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services