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Herausgeber: 
  • Nicholas Heller
  • Emanuele Trucco
  • Hien Van Nguyen
  • Kevin Zhou
  • Jaime Cardoso
  • Vishal Patel
  • Diana Mateus
  • Ricardo Cruz
  • Pedro Henriques Abreu
  • Veronika Cheplygina
  • Raphael Sznitman
  • Steve Jiang
  • Ngan Le
  • Khoa Luu
  • Badri Roysam
  • Samaneh Abbasi
  • Ivana Isgum
  • Wilson Silva
  • Jose Pereira Amorim
  • Interpretable and Annotation-Efficient Learning for Medical Image Computing: Third International Workshop, iMIMIC 2020, Second International Workshop, 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  Oktober 2020  
    Genre:  EDV / Informatik 
     
    ArtificialIntelligence / bioinformatics / Classification / Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften / Computervision / DeepLearning / imageanalysis / imageprocessing
    ISBN:  9783030611651 
    EAN-Code: 
    9783030611651 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 18 mm 
    Gewicht:  482 gr 
    Seiten:  316 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    This book constitutes the refereed joint proceedings of the Third International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2020, the Second International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2020, and the 5th International Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, LABELS 2020, held in conjunction with the 23rd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The 8 full papers presented at iMIMIC 2020, 11 full papers to MIL3ID 2020, and the 10 full papers presented at LABELS 2020 were carefully reviewed and selected from 16 submissions to iMIMIC, 28 to MIL3ID, and 12 submissions to LABELS. The iMIMIC papers focus on introducing the challenges and opportunities related to the topic of interpretability of machine learning systems in the context of medical imaging and computer assisted intervention. MIL3ID deals with best practices in medical image learning with label scarcity and data imperfection. The LABELS papers present a variety of approaches for dealing with a limited number of labels, from semi-supervised learning to crowdsourcing.

      



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