Deep Learning and Data Labeling for Medical Applications First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings /

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First Workshop on Large-Scale Annotation of Biomedical Data and Expert Label S...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Carneiro, Gustavo (Editor ), Mateus, Diana (Editor ), Peter, Loïc (Editor ), Bradley, Andrew (Editor ), Tavares, João Manuel R. S. (Editor ), Belagiannis, Vasileios (Editor ), Papa, João Paulo (Editor ), Nascimento, Jacinto C. (Editor ), Loog, Marco (Editor ), Lu, Zhi (Editor ), Cardoso, Jaime S. (Editor ), Cornebise, Julien (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edición:1st ed. 2016.
Colección:Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 10008
Temas:
Computer vision.
Pattern recognition systems.
Artificial intelligence.
Computer graphics.
Medical informatics.
Computer Vision.
Automated Pattern Recognition.
Artificial Intelligence.
Computer Graphics.
Health Informatics.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Active learning
  • Semi-supervised learning
  • Reinforcement learning
  • Domain adaptation and transfer learning
  • Crowd-sourcing annotations and fusion of labels from different sources
  • Data augmentation
  • Modelling of label uncertainty
  • Visualization and human-computer interaction
  • Image description
  • Medical imaging-based diagnosis
  • Medical signal-based diagnosis
  • Medical image reconstruction and model selection using deep learning techniques
  • Meta-heuristic techniques for fine-tuning
  • Parameter in deep learning-based architectures
  • Applications based on deep learning techniques.

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