检索结果-内蒙古大学图书馆

International Conference on Microelectronics, ICM

作者： Paolo Meloni Daniela Loi Gianfranco Deriu Andy D. Pimentel Dolly Saprat Maura Pintort Battista Biggio Oscar Ripolles David Solans Francesco Conti Luca Benini Todor Stefanov Svetlana Minakova Bernhard Moser Natalia Shepeleva Michael Masin Francesca Palumbo Nikos Fragoulis Ilias Theodorakopoulos Department of Electrical and Electronic Engineering University of Cagliari Italy Institute of Informatics University of Amsterdam The Netherlands Pluribus One Italy CA Technologies Spain Integrated Systems Laboratory ETH Zurich Switzerland Institute of Advanced Computer Science Leiden University The Netherlands Software Competence Center Hagenberg Austria IBM Research - Haifa Israel IDEA Lab University of Sassari Italy IRIDA Labs Computer Vision Systems Greece

ISBN: (纸本)9781538681688;9781538681671

The use of Deep Learning (DL) algorithms is increasingly evolving in many application domains. Despite the rapid growing of algorithm size and complexity, performing DL inference at the edge is becoming a clear trend to cope with low latency, privacy and bandwidth constraints. Nevertheless, traditional implementation on low-energy computing nodes often requires experience-based manual intervention and trial-and-error iterations to get to a functional and effective solution. This work presents a computer-aided design (CAD) support for effective implementation of DL algorithms on embedded systems, aiming at automating different design steps and reducing cost. The proposed tool flow comprises capabilities to consider architecture-and hardware-related variables at very early stages of the development process, from pre-training hyperparameter optimization and algorithm configuration to deployment, and to adequately address security, power efficiency and adaptivity requirements. This paper also presents some preliminary results obtained by the first implementation of the optimization techniques supported by the tool flow.

关键词： Tools Inference algorithms Engines computer architecture Computational modeling Microelectronics Training

来源：评论

学校读者我要写书评

暂无评论

Introducing the HOBBIT platform into the Ontology Alignment Evaluation campaign 13

Introducing the HOBBIT platform into the Ontology Alignment ...

引用

13th International Workshop on Ontology Matching, OM 2018

作者： Jiménez-Ruiz, Ernesto Saveta, Tzanina Zamazal, Ondřej Hertling, Sven Röder, Michael Fundulaki, Irini Ngomo, Axel-Cyrille Ngonga Sherif, Mohamed Ahmed Annane, Amina Bellahsene, Zohra Ben Yahia, Sadok Diallo, Gayo Faria, Daniel Kachroudi, Marouen Khiat, Abderrahmane Lambrix, Patrick Li, Huanyu Mackeprang, Maximilian Mohammadi, Majid Rybinski, Maciej Balasubramani, Booma Sowkarthiga Trojahn, Cassia Alan Turing Institute London United Kingdom Department of Informatics University of Oslo Norway Institute of Computer Science - FORTH Greece University of Economics Prague Czech Republic Data and Web Science Group University of Mannheim Germany Paderborn University Data Science Group Pohlweg 51 PaderbornD-33098 Germany Ecole Nationale Superieure d'Informatique Alger Algeria LIRMM Universit de Montpellier CNRS Montpellier France Université de Tunis El Manar LIPAH-LR 1ES14 Tunis2092 Tunisia BPH Center INSERM U1219 Team ERIAS LaBRI UMR5800 University of Bordeaux France Instituto Gulbenkian de Ciência Portugal Human-Centered Computing Freie Universitat Berlin Berlin4195 Germany Linköping University Swedish e-Science Research Centre Linköping Sweden Delft University of Technology Netherlands Dpto. Lenguajes y Ciencias de la Computación University of Malaga Spain ADVIS Lab. Department of Computer Science University of Illinois Chicago United States Institut de Recherche en Informatique de Toulouse France

This paper describes the Ontology Alignment Evaluation Initiative 2017.5 pre-campaign. Like in 2012, when we transitioned the evaluation to the SEALS platform, we have also conducted a pre-campaign to assess the feasibility of moving to the HOBBIT platform. We report the experiences of this pre-campaign and discuss the future steps for the OAEI. © 2018 CEUR-WS.

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

CovidCTNet: An open-source deep learning approach to identify Covid-19 using CT image

arXiv

引用

arXiv 2020年

作者： Javaheri, Tahereh Homayounfar, Morteza Amoozgar, Zohreh Reiazi, Reza Homayounieh, Fatemeh Abbas, Engy Laali, Azadeh Radmard, Amir Reza Gharib, Mohammad Hadi Mousavi, Seyed Ali Javad Ghaemi, Omid Babaei, Rosa Mobin, Hadi Karimi Hosseinzadeh, Mehdi Jahanban-Esfahlan, Rana Seidi, Khaled Kalra, Mannudeep K. Zhang, Guanglan Chitkushev, L.T. Haibe-Kains, Benjamin Malekzadeh, Reza Rawassizadeh, Reza Health Informatics Lab Metropolitan College Boston University Boston United States Department of Biomedical Engineering Amirkabir University of Technology Tehran Iran Department of Radiation Oncology Massachusetts General Hospital Harvard Medical School Boston United States Princess Margaret Cancer Centre University of Toronto Toronto Canada Department of Medical Biophysics University of Toronto Toronto Canada Department of Medical Physics School of Medicine Iran university of Medical Sciences Department of Radiology Massachusetts General Hospital Harvard Medical School Boston United States Joint Department of Medical Imaging University of Toronto Toronto Canada Department of Infectious Diseases Firoozgar Hospital Iran University of Medical Sciences Tehran Iran Department of Radiology Shariati Hospital Tehran University of Medical Sciences Tehran Iran Department of Radiology Gloestan Rheumatology Research Center Golestan University of Medical Sciences Gorgan Iran Department of Internal Medicine Iran University of Medical Sciences Tehran Iran Department of Radiology Iran University of Medical Sciences Tehran Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Medical Biotechnology School of Advanced Medical Sciences Tabriz University of Medical Sciences Tabriz Iran Department of Computer Science Metropolitan College Boston University Boston United States Department of Computer Science University of Toronto TorontoON Canada Ontario Institute for Cancer Research TorontoON Canada Vector Institute for Artificial Intelligence TorontoON Canada Digestive Disease Research Center Tehran University of Medical Sciences Tehran Iran

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Improved strategies to counter the COVID-19 pandemic: Lockdowns vs. primary and community healthcare

引用

Toxicology Reports 2021年 8卷 1-9页

作者： Farsalinos, Konstantinos Poulas, Konstantinos Kouretas, Dimitrios Vantarakis, Apostolos Leotsinidis, Michalis Kouvelas, Dimitrios Docea, Anca Oana Kostoff, Ronald Gerotziafas, Grigorios T. Antoniou, Michael N. Polosa, Riccardo Barbouni, Anastastia Yiakoumaki, Vassiliki Giannouchos, Theodoros V. Bagos, Pantelis G. Lazopoulos, George Izotov, Boris N. Tutelyan, Victor A. Aschner, Michael Hartung, Thomas Wallace, Heather M. Carvalho, Félix Domingo, Jose L. Tsatsakis, Aristides Laboratory of Molecular Biology and Immunology Department of Pharmacy University of Patras Panepistimiopolis 26500 Greece School of Public Health University of West Attica L Alexandras 196A Athens 11521 Greece Department of Biochemistry and Biotechnology University of Thessaly Larisa 41500 Greece School of Medicine University of Patras Panepistimiopolis 26500 Greece Lab. of Public Health Medical School University of Patras University Campus 26504 Greece Laboratory of Clinical Pharmacology School of Medicine Aristotle University of Thessaloniki Thessaloniki 54124 Greece Department of Toxicology University of Medicine and Pharmacy of Craiova Craiova 200349 Romania School of Public Policy Georgia Institute of Technology Gainesville 20155 VA United States Sorbonne Université INSERM UMR_S 938 Group de recherche « Cancer-Hemostasis-Angiogenesis » Centre de recherche Saint-Antoine CRSA Centre de Thrombose Tenon-Saint Antoine University Hospitals Assistance publique Hôpitaux de Paris France Gene Expression and Therapy Group King's College London Department of Medical and Molecular Genetics School of Basic & Medical Biosciences 8th Floor Tower Wing Guy's Hospital Great Maze Pond London SE1 9RT United Kingdom Department of Clinical and Experimental Medicine University of Catania Via S. Sofia Catania 97 95131 Italy Centro Prevenzione Cura Tabagismo Center of Excellence for the Acceleration of Harm Reduction University of Catania Catania 95123 Italy Department of History Archaeology and Social Anthropology University of Thessaly Volos 38221 Greece Pharmacotherapy Outcomes Research Center College of Pharmacy University of Utah Salt Lake City UT United States Department of Computer Science and Biomedical Informatics University of Thessaly Lamia 35100 Greece Department of Cardiac Surgery University Hospital of Heraklion Crete Greece Department of Analytical Toxicology Pharmaceutical Chemistry and Pharmacognosy Sechenov University Moscow 1199

COVID-19 pandemic mitigation strategies are mainly based on social distancing measures and healthcare system reinforcement. However, many countries in Europe and elsewhere implemented strict, horizontal lockdowns because of extensive viral spread in the community which challenges the capacity of the healthcare systems. However, strict lockdowns have various untintended adverse social, economic and health effects, which have yet to be fully elucidated, and have not been considered in models examining the effects of various mitigation measures. Unlike commonly suggested, the dilemma is not about health vs wealth because the economic devastation of long-lasting lockdowns will definitely have adverse health effects in the population. Furthermore, they cannot provide a lasting solution in pandemic containment, potentially resulting in a vicious cycle of consecutive lockdowns with in-between breaks. Hospital preparedness has been the main strategy used by governments. However, a major characteristic of the COVID-19 pandemic is the rapid viral transmission in populations with no immunity. Thus, even the best hospital system could not cope with the demand. Primary, community and home care are the only viable strategies that could achieve the goal of pandemic mitigation. We present the case example of Greece, a country which followed a strategy focused on hospital preparedness but failed to reinforce primary and community care. This, along with strategic mistakes in epidemiological surveillance, resulted in Greece implementing a second strict, horizontal lockdown and having one of the highest COVID-19 death rates in Europe during the second wave. We provide recommendations for measures that will reinstate primary and community care at the forefront in managing the current public health crisis by protecting hospitals from unnecessary admissions, providing primary and secondary prevention services in relation to COVID-19 and maintaining population health through treatment of n

关键词： COVID-19 pandemic Horizontal lockdowns Hospital preparedness Primary care

来源：评论

学校读者我要写书评

暂无评论

The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)

arXiv

引用

arXiv 2023年

作者： Kazerooni, Anahita Fathi Khalili, Nastaran Liu, Xinyang Haldar, Debanjan Jiang, Zhifan Anwar, Syed Muhammed Albrecht, Jake Adewole, Maruf Anazodo, Udunna Anderson, Hannah Bagheri, Sina Baid, Ujjwal Bergquist, Timothy Borja, Austin J. Calabrese, Evan Chung, Verena Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Haldar, Shuvanjan Iglesias, Juan Eugenio Janas, Anastasia Johansen, Elaine Jones, Blaise V. Kofler, Florian labella, Dominic Lai, Hollie Anne Van Leemput, Koen Li, Hongwei Bran Maleki, Nazanin McAllister, Aaron S. Meier, Zeke Menze, Bjoern Moawad, Ahmed W. Nandolia, Khanak K. Pavaine, Julija Piraud, Marie Poussaint, Tina Prabhu, Sanjay P. Reitman, Zachary Rodriguez, Andres Rudie, Jeffrey D. Sanchez-Montano, Mariana Shaikh, Ibraheem Salman Shah, Lubdha M. Sheth, Nakul Shinohara, Russel Taki Tu, Wenxin Viswanathan, Karthik Wang, Chunhao Ware, Jeffrey B. Wiestler, Benedikt Wiggins, Walter Zapaishchykova, Anna Aboian, Mariam Bornhorst, Miriam de Blank, Peter Deutsch, Michelle Fouladi, Maryam Hoffman, Lindsey Kann, Benjamin Lazow, Margot Mikael, Leonie Nabavizadeh, Ali Packer, Roger Resnick, Adam Rood, Brian Vossough, Arastoo Bakas, Spyridon Linguraru, Marius George Children’s Hospital of Philadelphia PhiladelphiaPA United States Department of Neurosurgery University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Neurosurgery Thomas Jefferson University Hospital PA United States Sage Bionetworks United States Lab Crestview Radiology Lagos Nigeria McGill University MontrealQC Canada Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Neurosurgery The University of Southern California CA United States Department of Radiology Duke University Medical Center United States Mayo Clinic MN United States Center for Global Health Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Biomedical Engineering Rutgers University New BrunswickNJ United States Athinoula A Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Yale University New HavenCT United States PrecisionFDA U.S. Food and Drug Administration Silver SpringMD United States Cincinnati Children’s Hospital Medical Center United States Helmholtz AI Helmholtz Munich Germany Department of Radiation Oncology Duke University Medical Center United States Department of Radiology Children’s Health Orange County CA United States Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark Department of Radiol

Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors. © 2023, CC BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Visual Homing via Guided Locality Preserving Matching

Visual Homing via Guided Locality Preserving Matching

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiayi Ma Ji Zhao Junjun Jiang Huabing Zhou Yu Zhou Zheng Wang Xiaojie Guo Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China ReadSense Ltd. China School of Computer Science China University of Geosciences Wuhan China Hubei Key Lab of Intelligent Robot Wuhan Institute of Technology Wuhan China Institute of Sensing Technology and Business Beijing University of Posts and Telecommunications Beijing China National Institute of Informatics Tokyo Japan School of Computer Software Tianjin University Tianjin China

This study proposes a simple yet surprisingly effective feature matching approach, termed as guided locality preserving matching (GLPM), for visual homing of panoramic images. The key idea of our approach is merely to preserve the neighborhood structures of potential true matches between two panoramic images. We formulate it into a mathematical model, and derive a simple closed-form solution with linearithmic time and linear space complexities. This enables our method to accomplish the mismatch removal from hundreds of putative correspondences in only a few milliseconds. To handle extremely large proportions of outliers, we further design a guided matching strategy based on the proposed method, using the matching result on a small putative set with a high inlier ratio to guide the matching on a large putative set. This strategy can also significantly boost true matches without sacrifice in accuracy. To apply our GLPM to the visual homing problem, we develop a method for dense motion flow estimation from sparse feature matches based on Tikhonov regularization. Moreover, the focus-of-contraction/focus-of-expansion is derived to determine homing directions. The effectiveness of our method is demonstrated on a panoramic database in both feature matching and visual homing.

关键词： Visualization Feature extraction Electronic mail Cost function Measurement Closed-form solutions Intelligent robots

来源：评论

学校读者我要写书评

暂无评论

Pathogenesis, symptomatology, and transmission of SARS-CoV-2 through analysis of viral genomics and structure

arXiv

引用

arXiv 2021年

作者： Rando, Halie M. MacLean, Adam L. Lee, Alexandra J. Lordan, Ronan Ray, Sandipan Bansal, Vikas Skelly, Ashwin N. Sell, Elizabeth Dziak, John J. Shinholster, Lamonica D'Agostino McGowan, Lucy Guebila, Marouen Ben Wellhausen, Nils Knyazev, Sergey Boca, Simina M. Capone, Stephen Qi, Yanjun Park, Yoson Sun, Yuchen Mai, David Boerckel, Joel D. Brueffer, Christian Byrd, James Brian Kamil, Jeremy P. Wang, Jinhui Velazquez, Ryan Szeto, Gregory L. Barton, John P. Goel, Rishi Raj Mangul, Serghei Lubiana, Tiago Gitter, Anthony Greene, Casey S. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States University of Colorado School of Medicine AuroraCO United States Department of Quantitative and Computational Biology University of Southern California Los AngelesCA United States Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Biotechnology Indian Institute of Technology Hyderabad Kandi Sangareddy Telangana 502285 India Biomedical Data Science and Machine Learning Group German Center for Neurodegenerative Diseases Tubingen72076 Germany Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Immunology University of Pennsylvania Perelman School of Medicine Philadelphia United States Edna Bennett Pierce Prevention Research Center The Pennsylvania State University University ParkPA United States LSH2126 Mercer University MaconGA United States Department of Mathematics and Statistics Wake Forest University Winston-SalemNC United States Department of Biostatistics Harvard School of Public Health BostonMA United States Georgia State University AtlantaGA United States Innovation Center for Biomedical Informatics Georgetown University Medical Center WashingtonDC United States St. George's University School of Medicine Saint George's Grenada Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Department of Orthopaedic Surgery Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Bioengineering University of Pennsylvania Philadelphia PhiladelphiaPA United States Department of Clinical Sciences L

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. © 2021, CC BY.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

Exploring the flexibility of everyday practices for shifting energy consumption through clockcast 17

Exploring the flexibility of everyday practices for shifting...

引用

29th Australian computer-Human Interaction Conference, OzCHI 2017

作者： Rasmussen, Majken K. Rasmussen, Mia Kruse Verdezoto, Nervo Brewer, Robert Nielsen, Laura L. Bouvin, Niels Olof Department of Engineering Aarhus University Aarhus8000 Denmark People Technology and Business Lab Alexandra Institute Aarhus8200 Denmark Department of Informatics University of Leicester LeicesterLE1 7RH United Kingdom Department of Computer Science Aarhus University Aarhus8200 Denmark

ISBN: (纸本)9781450353793

Encouraging sustainable living by raising awareness of resource consumption has long been a topic within HCI. However, getting people to change behavior when it comes to energy consumption is difficult. This is one of the major challenges ahead for future energy systems, in particular if resources are renewable and plentiful. We developed the ClockCast prototypes (web and clock forecast) to explore demand response and the flexibility potential of everyday practices. We wanted to reframe the conversation on demand response: from highlighting when not to use energy to highlighting when to use it. The ClockCast prototypes display the best times to use electricity, and they were complemented by proactive and positive suggestions. We conducted a pilot study with five different households to uncover the socio-technical challenges around shifting consumption and the participants' experiences with the prototypes. While the participants increased their awareness of the environmental implications of their actions, shifted some electricity use, and found the forecasts useful, some participants also reported newfound guilt when they did not follow the forecasts. © 2017 Association for Computing Machinery. All rights reserved.

关键词： Energy utilization

来源：评论

学校读者我要写书评

暂无评论

Prediction and understanding of soft proton contamination in XMM-Newton: A machine learning approach

arXiv

引用

arXiv 2020年

作者： Kronberg, Elena A. Gastaldello, Fabio Haaland, Stein Smirnov, Artem Berrendorf, Max Ghizzardi, Simona Kuntz, K.D. Sivadas, Nithin Allen, Robert C. Tiengo, Andrea Ilie, Raluca Huang, Yu Kistler, Lynn University of Munich Theresienstr. 41 Munich80333 Germany Milano via A. Corti 12 MilanoI-20133 Italy Birkeland Centre for Space Science University of Bergen Allégaten 55 Bergen5007 Norway Max Planck Institute for Solar System Research Justus-von-Liebig-Weg 3 Göttingen Germany German Research Centre for Geosciences Albert-Einstein-Strae 42-46 Potsdam14473 Germany Geophysical Center of the Russian Academy of Sciences Molodezhnaya St. 3 Moscow119296 Russia Institute of Informatics University of Munich Oettingenstrae 67 Munich80538 Germany Henry A. Rowland Department of Physics & Astronomy Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States Department of Electrical and Computer Engineering Boston University 8 Saint Mary’s Street BostonMA02134 United States Johns Hopkins University Applied Physics Lab 11100 Johns Hopkins Rd LaurelMD20723 United States Scuola Universitaria Superiore IUSS Pavia piazza della Vittoria 15 PaviaI-27100 Italy INFN Sezione di Pavia via A. Bassi 6 PaviaI-27100 Italy University of Illinois at Urbana-Champaign ECEB 306 N. Wright Street 5054 UrbanaIL61801 United States Space Science Center University of New Hampshire Morse Hall Rm 408 DurhamNH03824 United States

One of the major and unfortunately unforeseen sources of background for the current generation of X-ray telescopes are few tens to hundreds of keV (soft) protons concentrated by the mirrors. One such telescope is the European Space Agency’s (ESA) X-ray Multi-Mirror Mission (XMM-Newton). Its observing time lost due to background contamination is about 40%. This loss of observing time affects all the major broad science goals of this observatory, ranging from cosmology to astrophysics of neutron stars and black holes. The soft proton background could dramatically impact future large X-ray missions such as the ESA planned Athena missiona). Physical processes that trigger this background are still poorly understood. We use a Machine Learning (ML) approach to delineate related important parameters and to develop a model to predict the background contamination using 12 years of XMM observations. As predictors we use the location of satellite, solar and geomagnetic activity parameters. We revealed that the contamination is most strongly related to the distance in southern direction, Z, (XMM observations were in the southern hemisphere), the solar wind radial velocity and the location on the magnetospheric magnetic field lines. We derived simple empirical models for the first two individual predictors and an ML model which utilizes an ensemble of the predictors (Extra Trees Regressor) and gives better performance. Based on our analysis, future missions should minimize observations during times associated with high solar wind speed and avoid closed magnetic field lines, especially at the dusk flank region in the southern hemisphere. Copyright © 2020, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI

arXiv

引用

arXiv 2023年

作者： Moawad, Ahmed W. Janas, Anastasia Baid, Ujjwal Ramakrishnan, Divya Saluja, Rachit Ashraf, Nader Jekel, Leon Amiruddin, Raisa Adewole, Maruf Albrecht, Jake Anazodo, Udunna Aneja, Sanjay Anwar, Syed Muhammad Bergquist, Timothy Calabrese, Evan Chiang, Veronica Chung, Verena Conte, Gian Marco Marco Dako, Farouk Eddy, James Ezhov, Ivan Familiar, Ariana Farahani, Keyvan Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Kazerooni, Anahita Fathi Kofler, Florian Krantchev, Kiril labella, Dominic Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Link, Katherine E. Liu, Xinyang Maleki, Nazanin Meier, Zeke Menze, Bjoern H. Moy, Harrison Osenberg, Klara Piraud, Marie Reitman, Zachary Shinohara, Russel Takeshi Tahon, Nourel hoda Nada, Ayman Velichko, Yuri S. Wang, Chunhao Wiestler, Benedikt Wiggins, Walter Shafique, Umber Willms, Klara Avesta, Arman Bousabarah, Khaled Chakrabarty, Satrajit Gennaro, Nicolo Holler, Wolfgang Kaur, Manpreet LaMontagne, Pamela Lin, MingDe Lost, Jan Marcus, Daniel S. Maresca, Ryan Merkaj, Sarah Nada, Ayaman Pedersen, Gabriel Cassinelli von Reppert, Marc Sotiras, Aristeidis Teytelboym, Oleg Tillmans, Niklas Westerhoff, Malte Youssef, Ayda Godfrey, Devon Floyd, Scott Rauschecker, Andreas Villanueva-Meyer, Javier Pflüger, Irada Cho, Jaeyoung Bendszus, Martin Brugnara, Gianluca Cramer, Justin Perez-Carillo, Gloria Guzman J. Johnson, Derek R. Kam, Anthony Kwan, Benjamin Yin Ming Lai, Lillian Lall, Neil U. Memon, Fatima Patro, Satya Narayana Petrovic, Bojan So, Tiffany Y. Thompson, Gerard Wu, Lei Schrickel, E. Brooke Bansal, Anu Barkhof, Frederik Besada, Cristina Chu, Sammy Druzgal, Jason Dusoi, Alexandru Farage, Luciano Feltrin, Fabricio Fong, Amy Fung, Steve H. Gray, R. Ian Ikuta, Ichiro Iv, Michael Postma, Alida A. Mahajan, Amit Joyner, David Krumpelman, Chase Letourneau-Guillon, Laurent Lincoln, Christie M. Maros, Mate E. Miller, Elka Morón, Fanny Nimchinsky, Esther A. Ozsarlak, Ozkan Patel, Uresh Rohatgi, Saurabh Saha, Atin Sayah, Anousheh Schwartz, Eric D. Shih, Robert Shiroishi, Mark Trinity health Mid Atlantic Hosiptals DarbyPA United States Department of Radiology and Biomedical Imaging Yale School of Medicine New HavenCT United States Division of Computational Pathology Department of Pathology and Laboratory Medicine School of Medicine Indiana University IndianapolisIN United States Department of Electical and Computer Engineering Cornell University Cornell Tech New YorkNY United States Department of Radiology Weill Cornell Medicine New YorkNY United States ImagineQuant Children’s Hospital of Philadelphia PhiladelphiaPA United States College of Medicine Alfaisal University Riyadh Saudi Arabia DKFZ Division of Translational Neurooncology The WTZ German Cancer Consortium DKTK Partner Site University Hospital Essen Essen Germany Medical Artificial Intelligence Lab Crestview Radiology Lagos Nigeria Sage Bionetworks SeattleWA United States Montreal Neurological Institute McGill University Montreal Canada lab Crestview Radiology Lagos Nigeria Department of Therapeutic Radiology Yale School of Medicine New HavenCT United States Sheikh Zayed Institute for Pediatric Surgical Innovation Children’s National Hospital WashingtonDC United States Department of Radiology Mayo Clinic RochesterMN United States Department of Radiology Duke University Medical Center DurhamNC United States Department of Neurosurgery Yale School of Medicine New HavenCT United States Center for Global Health Perelman School of Medicine University of Pennsylvania PA United States Department of Informatics Technical University Munich Germany Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital BostonMA United States Children’s National Hospital WashingtonDC United States PrecisionFDA U.S. Food and Drug Administration S

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well-annotated, diverse d

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：