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Herausgeber: 
  • Nicholas Heller
  • Emanuele Trucco
  • Yiyu Shi
  • Luping Zhou
  • Diana Mateus
  • Veronika Cheplygina
  • Raphael Sznitman
  • Yiming Xiao
  • Hassan Rivaz
  • Ingerid Reinertsen
  • Matthieu Chabanas
  • Danny Chen
  • X. Sharon Hu
  • Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Interventio 
     

    (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:  November 2019  
    Genre:  EDV / Informatik 
     
    ArtificialIntelligence / Computeranwendungen in Industrie und Technologie / DeepLearning / hardware-aideddiagnosis / hardware-assistedintervention / imageanalysis / imagecompression / imagequality
    ISBN:  9783030336417 
    EAN-Code: 
    9783030336417 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 10 mm 
    Gewicht:  277 gr 
    Seiten:  176 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book constitutes the refereed joint proceedings of the 4th International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2019, the First International Workshop on Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention, HAL-MICCAI 2019, and the Second International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 8 papers presented at LABELS 2019, the 5 papers presented at HAL-MICCAI 2019, and the 3 papers presented at CuRIOUS 2019 were carefully reviewed and selected from numerous submissions. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing. The HAL-MICCAI papers cover a wide set of hardware applications inmedical problems, including medical image segmentation, electron tomography, pneumonia detection, etc. The CuRIOUS papers provide a snapshot of the current progress in the field through extended discussions and provide researchers an opportunity to characterize their image registration methods on newly released standardized datasets of iUS-guided brain tumor resection.

      



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