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Herausgeber: 
  • Mohamad Habes
  • Hongzhi Wang
  • Luping Zhou
  • Stefanie Speidel
  • Daniel Hashimoto
  • Duygu Sarikaya
  • Anand Malpani
  • Tommy Löfstedt
  • Seyed Mostafa Kia
  • Kerstin Ritter
  • OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging: Second International Workshop, OR 2.0 2019, and Second Internat 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  Oktober 2019  
    Genre:  EDV / Informatik 
     
    ArtificialIntelligence / Computervision / imageanalysis / imagereconstruction / imagingsystems / multi-spectral / RemoteSensing / visualqualities
    ISBN:  9783030326944 
    EAN-Code: 
    9783030326944 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 8 mm 
    Gewicht:  213 gr 
    Seiten:  132 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book constitutes the refereed proceedings of the Second International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the Second International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For OR 2.0 all 6 submissions were accepted for publication. They aim to highlight the potential use of machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors, wearable and implantable electronics and robots, visual attention models, cognitive models, decision support networks to enhance surgical procedural assistance, context-awareness and team communication in the operating theater, human-robot collaborative systems, and surgical training and assessment. MLCN 2019 accepted 6 papers out of 7 submissions for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. Chapter 5 is available open access under a Creative Commons Attribution 4.0 International License via Springerlink.

      



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