检索结果-内蒙古大学图书馆

arXiv 2020年

作者： DeJongh, Don F. DeJongh, Ethan A. Rykalin, Victor DeFillippo, Greg Pankuch, Mark Best, Andrew W. Coutrakon, George Duffin, Kirk L. Karonis, Nicholas T. Ordoñez, Caesar E. Sarosiek, Christina Schulte, Reinhard W. Winans, John R. Block, Alec M. Hentz, Courtney L. Welsh, James S. ProtonVDA LLC NapervilleIL60563 United States Northwestern Medicine Chicago Proton Center WarrenvilleIL60555 United States Department of Physics Northern Illinois University DeKalbIL60115 United States Department of Computer Science Northern Illinois University DeKalbIL60115 United States Argonne National Laboratory Data Science and Learning Division ArgonneIL60439 United States Loma Linda University Loma LindaCA92350 United States Edward Hines Jr VA Medical Center Radiation Oncology Service HinesIL60141 United States Department of Radiation Oncology Loyola University Stritch School of Medicine MaywoodIL60153 United States

Purpose: Currently, calculations of proton range in proton therapy patients are based on a conversion of CT Hounsfield Units of patient tissues into proton relative stopping power. Uncertainties in this conversion necessitate larger proximal and distal planned target volume margins. Proton CT can potentially reduce these uncertainties by directly measuring proton stopping power. We aim to demonstrate proton CT imaging with complex porcine samples, to analyze in detail three-dimensional regions of interest, and to compare proton stopping powers directly measured by proton CT to those determined from x-ray CT scans. Methods: We have used a prototype proton imaging system with single proton tracking to acquire proton radiography and proton CT images of a sample of porcine pectoral girdle and ribs, and a pig's head. We also acquired close in time x-ray CT scans of the same samples, and compared proton stopping power measurements from the two modalities. In the case of the pig's head, we obtained x-ray CT scans from two different scanners, and compared results from high-dose and low-dose settings. Results: Comparing our reconstructed proton CT images with images derived from x-ray CT scans, we find agreement within 1% to 2% for soft tissues, and discrepancies of up to 6% for compact bone. We also observed large discrepancies, up to 40%, for cavitated regions with mixed content of air, soft tissue, and bone, such as sinus cavities or tympanic bullae. Conclusions: Our images and findings from a clinically realistic proton CT scanner demonstrate the potential for proton CT to be used for low-dose treatment planning with reduced margins. Copyright © 2020, The Authors. All rights reserved.

关键词： Computerized tomography

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Unintended consequences: Factors influencing oil palm plantation expansion

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IOP Conference Series: Earth and Environmental science 2020年第1期538卷

作者： S Wongsai J Keson N Wongsai Thammasat University Research Unit in Data Learning Division of Mathematics and Statistics Faculty of Science and Technology Thammasat University Pathumthani 12120 Thailand Faculty of Technology and Environment Prince of Songkla University Phuket Campus Phuket 83120 Thailand Department of Mathematics and Computer Science Faculty of Science and Technology Prince of Songkla University Pattani Campus Pattani 94000 Thailand

We investigated the landscape variables affecting the current dramatic expansion of oil palm plantations in Lam Thap district Krabi Province Thailand. THEOS satellite data was used to map land use classifications using the support vector machine method. Seven land use classes were consolidated into oil palm plantations and other rural uses. A logistic regression model was then applied to search for the relationship between two land use classes and the landscape variables of slope (three classes) and soil drainage (four classes). Overall, slope and drainage were statistically significant in explaining oil palm plantation expansion. Flat areas with poor drainage were the strongest factors. This contradicts the known optimal agronomic requirements for oil palms of well drainage. We suggest that what is observed is an unintended consequence of government oil palm support policies.

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Skluma: An Extensible Metadata Extraction Pipeline for Disorganized data

Skluma: An Extensible Metadata Extraction Pipeline for Disor...

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IEEE International Conference on e-science and Grid Computing

作者： Tyler J. Skluzacek Rohan Kumar Ryan Chard Galen Harrison Paul Beckman Kyle Chard Ian T. Foster University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Argonne IL USA University of Chicago Chicago IL US

To mitigate the effects of high-velocity data expansion and to automate the organization of filesystems and data repositories, we have developed Skluma-a system that automatically processes a target filesystem or repository, extracts content-and context-based metadata, and organizes extracted metadata for subsequent use. Skluma is able to extract diverse metadata, including aggregate values derived from embedded structured data; named entities and latent topics buried within free-text documents; and content encoded in images. Skluma implements an overarching probabilistic pipeline to extract increasingly specific metadata from files. It applies machine learning methods to determine file types, dynamically prioritizes and then executes a suite of metadata extractors, and explores contextual metadata based on relationships among files. The derived metadata, represented in JSON, describes probabilistic knowledge of each file that may be subsequently used for discovery or organization. Skluma's architecture enables it to be deployed both locally and used as an on-demand, cloud-hosted service to create and execute dynamic extraction workflows on massive numbers of files. It is modular and extensible-allowing users to contribute their own specialized metadata extractors. Thus far we have tested Skluma on local filesystems, remote FTP-accessible servers, and publicly-accessible Globus endpoints. We have demonstrated its efficacy by applying it to a scientific environmental data repository of more than 500,000 files. We show that we can extract metadata from those files with modest cloud costs in a few hours.

关键词： Metadata data mining Crawlers Containers Pipelines Null value

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Generating Synthetic Computed Tomography for Radiotherapy: SynthRAD2023 Challenge Report

arXiv

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arXiv 2024年

作者： Huijben, Evi M.C. Terpstra, Maarten L. Galapon, Arthur Pai, Suraj Thummerer, Adrian Koopmans, Peter Afonso, Manya van Eijnatten, Maureen Gurney-Champion, Oliver Chen, Zeli Zhang, Yiwen Zheng, Kaiyi Li, Chuanpu Pang, Haowen Ye, Chuyang Wang, Runqi Song, Tao Fan, Fuxin Qiu, Jingna Huang, Yixing Ha, Juhyung Park, Jong Sung Alain-Beaudoin, Alexandra Bériault, Silvain Yu, Pengxin Guo, Hongbin Huang, Zhanyao Li, Gengwan Zhang, Xueru Fan, Yubo Liu, Han Xin, Bowen Nicolson, Aaron Zhong, Lujia Deng, Zhiwei Müller-Franzes, Gustav Khader, Firas Li, Xia Zhang, Ye Hémon, Cédric Boussot, Valentin Zhang, Zhihao Wang, Long Bai, Lu Wang, Shaobin Mus, Derk Kooiman, Bram Sargeant, Chelsea A.H. Henderson, Edward G.A. Kondo, Satoshi Kasai, Satoshi Karimzadeh, Reza Ibragimov, Bulat Helfer, Thomas Dafflon, Jessica Chen, Zijie Wang, Enpei Perko, Zoltan Maspero, Matteo Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands Radiotherapy Department University Medical Center Utrecht Utrecht Netherlands Computational Imaging Group for MR Diagnostics & Therapy University Medical Center Utrecht Utrecht Netherlands Department of Radiation Oncology University Medical Center Groningen University of Groningen Groningen Netherlands GROW School for Oncology Maastricht University Medical Centre Maastricht Netherlands Department of Radiation Oncology LMU University Hospital LMU Munich Munich Germany Department of Radiation Oncology Radboud University Medical Center Nijmegen Netherlands Wageningen University & Research Wageningen Plant Research Wageningen Netherlands Department of Radiology and Nuclear Medicine Amsterdam UMC Location University of Amsterdam Amsterdam Netherlands Cancer Center Amsterdam Imaging and Biomarkers Amsterdam Netherlands School of Biomedical Engineering Southern Medical University Guangzhou China School of Integrated Circuits and Electronics Beijing Institute of Technology Beijing China School of Biomedical Engineering ShanghaiTech University Shanghai China Fudan University Shanghai China Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Indiana University Bloomington United States Advanced Development Engineering Elekta Ltd Montreal Canada Infervision Medical Technology Co. Ltd. Beijing China Department of Biomedical Engineering Shantou University China Independent researchers Department of Computer Science Vanderbilt University Nashville United States Australian e-Health Research Centre CSIRO HerstonQLD Australia Los AngelesCA United States University Hospital Aachen Aachen Germany Center for Proton Therapy Paul Scherrer Institut Villigen Switzerland Department of Computer Science ETH Zurich Zurich Switzerland University Rennes 1 CLCC Eugène Marquis INSERM LTSI Rennes France Subtle Medical Shanghai China MedMind Technology Co.

Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data crucial for accurate dose calculations. However, accurately representing patient anatomy is challenging, especially in adaptive radiotherapy, where CT is not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it lacks electron density information, while cone beam CT (CBCT) lacks direct electron density calibration and is mainly used for patient positioning. Adopting MRI-only or CBCT-based adaptive radiotherapy eliminates the need for CT planning but presents challenges. Synthetic CT (sCT) generation techniques aim to address these challenges by using image synthesis to bridge the gap between MRI, CBCT, and CT. The SynthRAD2023 challenge was organized to compare synthetic CT generation methods using multi-center ground truth data from 1080 patients, divided into two tasks: 1) MRI-to-CT and 2) CBCT-to-CT. The evaluation included image similarity and dose-based metrics from proton and photon plans. The challenge attracted significant participation, with 617 registrations and 22/17 valid submissions for tasks 1/2. Top-performing teams achieved high structural similarity indices (≥ 0.87/0.90) and gamma pass rates for photon (≥ 98.1%/99.0%) and proton (≥ 97.3%/97.0%) plans. However, no significant correlation was found between image similarity metrics and dose accuracy, emphasizing the need for dose evaluation when assessing the clinical applicability of sCT. SynthRAD2023 facilitated the investigation and benchmarking of sCT generation techniques, providing insights for developing MRI-only and CBCT-based adaptive radiotherapy. It showcased the growing capacity of deep learning to produce high-quality sCT, reducing reliance on conventional CT for treatment planning. © 2024, CC

关键词： Radiotherapy

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Illinois Express Quantum Network (IEQNET): Metropolitan-scale experimental quantum networking over deployed optical fiber

arXiv

引用

arXiv 2021年

作者： Chung, Joaquin Kanter, Gregory Lauk, Nikolai Valivarthi, Raju Wu, Wenji Ceballos, Russell R. Peña, Cristián Sinclair, Neil Thomas, Jordan Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt LLC 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States Department of Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Dr. ChicagoIL60628 United States Pittsburgh Quantum Institute B4 Thaw Hall 4061 O'Hara Street PittsburghPA15260 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-nodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. © 2021, CC BY.

关键词： Optical fibers

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Star Cluster Classification using Deep Transfer learning with PHANGS-HST

arXiv

引用

arXiv 2023年

作者： Hannon, Stephen Whitmore, Bradley C. Lee, Janice C. Thilker, David A. Deger, Sinan Huerta, E.A. Wei, Wei Mobasher, Bahram Klessen, Ralf Boquien, Médéric Dale, Daniel A. Chevance, Mélanie Grasha, Kathryn Sanchez-Blazquez, Patricia Williams, Thomas Scheuermann, Fabian Groves, Brent Kim, Hwihyun Kruijssen, J.M. Diederik Department of Physics & Astronomy University of California RiversideCA92507 United States Space Telescope Science Institute BaltimoreMD21218 United States Gemini Observatory NSF’s NOIRLab 950 N. Cherry Avenue TucsonAZ85719 United States Steward Observatory University of Arizona TucsonAZ85721 United States Department of Physics and Astronomy The Johns Hopkins University BaltimoreMD21218 United States TAPIR California Institute of Technology PasadenaCA91125 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Zentrum für Astronomie der Universität Heidelberg Institut für Theoretische Astrophysik HeidelbergD-69120 Germany Universität Heidelberg Interdiszipliäres Zentrum für Wissenschaftliches Rechnen HeidelbergD-69120 Germany Instituto de Alta Investigación Universidad de Tarapacá Casilla 7D Arica Chile Department of Physics & Astronomy University of Wyoming LaramieWY82071 United States Research DAO coolresearch.io Research School of Astronomy and Astrophysics Australian National University CanberraACT2611 Australia Australia Departamento de Física de la Tierra y Astrofísica UCM Madrid28040 Spain Max Planck Institut für Astronomie Königstuhl17 Heidelberg69117 Germany Astronomisches Rechen-Institut Zentrum für Astronomie der Universität Heidelberg Mönchofstraße 12-14 HeidelbergD-69120 Germany International Centre for Radio Astronomy Research The University of Western Australia 7 Fairway CrawleyWA6009 Australia Research School of Astronomy and Astrophysics Australian National University Mt. Stromlo Observatory Weston CreekACT2611 Australia Technical University of Munich School of Engineering and Design Department of Aerospace and Geodesy Chair of Remote Sensing Technology Arcisstr. 21 Munich80333

Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable catalogues and thus also post-observation analysis. To address this problem, deep transfer learning has recently been used to create neural network models which accurately classify star cluster morphologies at production scale for nearby spiral galaxies (D 20 Mpc). Here, we use HST UV-optical imaging of over 20,000 sources in 23 galaxies from the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS) survey to train and evaluate two new sets of models: i) distance-dependent models, based on cluster candidates binned by galaxy distance (9–12 Mpc, 14–18 Mpc, 18–24 Mpc), and ii) distance-independent models, based on the combined sample of candidates from all galaxies. We find that the overall accuracy of both sets of models is comparable to previous automated star cluster classification studies (∼60–80 per cent) and show improvement by a factor of two in classifying asymmetric and multi-peaked clusters from PHANGS-HST. Somewhat surprisingly, while we observe a weak negative correlation between model accuracy and galactic distance, we find that training separate models for the three distance bins does not significantly improve classification accuracy. We also evaluate model accuracy as a function of cluster properties such as brightness, colour, and SED-fit age. Based on the success of these experiments, our models will provide classifications for the full set of PHANGS-HST candidate clusters (N∼200,000) for public release. © 2023, CC BY.

关键词： Galaxies

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Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA

arXiv

引用

arXiv 2023年

作者： Yang, Kaiyuan Musio, Fabio Ma, Yihui Juchler, Norman Paetzold, Johannes C. Al-Maskari, Rami Höher, Luciano Li, Hongwei Bran Hamamci, Ibrahim Ethem Sekuboyina, Anjany Shit, Suprosanna Huang, Houjing Prabhakar, Chinmay de la Rosa, Ezequiel Waldmannstetter, Diana Kofler, Florian Navarro, Fernando Menten, Martin Ezhov, Ivan Rueckert, Daniel Vos, Iris Ruigrok, Ynte Velthuis, Birgitta Kuijf, Hugo Hämmerli, Julien Wurster, Catherine Bijlenga, Philippe Westphal, Laura Bisschop, Jeroen Colombo, Elisa Baazaoui, Hakim Makmur, Andrew Hallinan, James Wiestler, Bene Kirschke, Jan S. Wiest, Roland Montagnon, Emmanuel Letourneau-Guillon, Laurent Galdran, Adrian Galati, Francesco Falcetta, Daniele Zuluaga, Maria A. Lin, Chaolong Zhao, Haoran Zhang, Zehan Ra, Sinyoung Hwang, Jongyun Park, Hyunjin Chen, Junqiang Wodzinski, Marek Müller, Henning Shi, Pengcheng Liu, Wei Ma, Ting Yalçin, Cansu Hamadache, Rachika E. Salvi, Joaquim Llado, Xavier Estrada, Uma Maria Lal-Trehan Abramova, Valeriia Giancardo, Luca Oliver, Arnau Liu, Jialu Huang, Haibin Cui, Yue Lin, Zehang Liu, Yusheng Zhu, Shunzhi Patel, Tatsat R. Tutino, Vincent M. Orouskhani, Maysam Wang, Huayu Mossa-Basha, Mahmud Zhu, Chengcheng Rokuss, Maximilian R. Kirchhoff, Yannick Disch, Nico Holzschuh, Julius Isensee, Fabian Maier-Hein, Klaus Sato, Yuki Hirsch, Sven Wegener, Susanne Menze, Bjoern Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Center for Computational Health Zurich University of Applied Sciences Zurich Switzerland Department of Neuroradiology University Hospital of Zurich Zurich Switzerland Department of Neurosurgery Zhongnan Hospital of Wuhan University Wuhan China Department of Computing Imperial College London London United Kingdom Helmholtz Center Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Harvard Medical School Boston United States School of Medicine Technical University of Munich Munich Germany Department of Informatics Technical University of Munich Munich Germany Helmholtz AI Helmholtz Munich Munich Germany Image Sciences Institute UMC Utrecht Utrecht Netherlands Department of Neurology UMC Utrecht Utrecht Netherlands Department of Radiology UMC Utrecht Utrecht Netherlands Department of Clinical Neurosciences Division of Neurosurgery Geneva University Hospitals Geneva Switzerland Department of Neurology University Hospital of Zurich Zurich Switzerland Department of Physiology University of Toronto Toronto Canada Department of Neurosurgery University Hospital of Zurich Zurich Switzerland Department of Diagnostic Imaging National University Hospital Singapore Department of Diagnostic and Interventional Neuroradiology University Hospital Berne University of Berne Berne Switzerland Montreal Canada Universitat Pompeu Fabra Barcelona Spain EURECOM Biot France Institute of Medical Technology Peking University Health Science Center Beijing China Hangzhou Genlight Medtech Co. Ltd. Hangzhou China Department of Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of Shanghai MediWorks Precision Instruments Co. Ltd. Shanghai China Institute of Informatics HES-SO Valais-Wallis Switzerland Department of Measurement and Electronics AGH University

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically. © 2023, CC BY-NC-ND.

关键词： Large datasets

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DLHub: Model and data Serving for science

arXiv

引用

arXiv 2018年

作者： Chard, Ryan Li, Zhuozhao Chard, Kyle Ward, Logan Babuji, Yadu Woodard, Anna Tuecke, Steven Blaiszik, Ben Franklin, Michael J. Foster, Ian Data Science and Learning Division Argonne National Laboratory ArgonneIL United States Department of Computer Science University of Chicago ChicagoIL United States Globus University of Chicago ChicagoIL United States

—While the Machine learning (ML) landscape is evolving rapidly, there has been a relative lag in the development of the "learning systems" needed to enable broad adoption. Furthermore, few such systems are designed to support the specialized requirements of scientific ML. Here we present the data and learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications. DLHub addresses two significant shortcomings in current systems. First, its self-service model repository allows users to share, publish, verify, reproduce, and reuse models, and addresses concerns related to model reproducibility by packaging and distributing models and all constituent components. Second, it implements scalable and low-latency serving capabilities that can leverage parallel and distributed computing resources to democratize access to published models through a simple web interface. Unlike other model serving frameworks, DLHub can store and serve any Python 3-compatible model or processing function, plus multiple-function pipelines. We show that relative to other model serving systems including TensorFlow Serving, SageMaker, and Clipper, DLHub provides greater capabilities, comparable performance without memoization and batching, and significantly better performance when the latter two techniques can be employed. We also describe early uses of DLHub for scientific applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

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Towards hybrid human-machine scientific information extraction

Towards hybrid human-machine scientific information extracti...

引用

New York Scientific data Summit (NYSDS)

作者： Roselyne Tchoua Aswathy Ajith Zhi Hong Logan Ward Kyle Chard Debra Audus Shrayesh Patel Juan de Pablo Ian Foster Department of Computer Science University of Chicago Chicago IL USA Data Science and Learning Division Argonne National Laboratory Argonne IL USA Materials Science and Engineering Division National Institute of Standards and Technology Gaithersburg MD USA Institute for Molecular Engineering University of Chicago Chicago IL USA

A wealth of valuable research data is locked within the millions of research articles published every year [1]. Extracting pertinent scientific facts (e.g., materials properties, known variants in genomics, population statistics etc.) from those articles has become an unmanageable task for researchers. This problem hinders the advancement of science, making it difficult to build on existing results, avoid unnecessary repetition, and to translate results into applications. Moreover, since these data are often loosely encoded in esoteric scientific articles intended for human consumption, they are, in general, not machine accessible. Thus, it is not often tractable to develop studies that automatically leverage this valuable information.

关键词： Polymers Task analysis Manuals data mining Temperature Crowdsourcing Informatics

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