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

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 / edited by Gustavo Carneiro, Diana Mateus, Loïc Peter, Andrew Bradley, João Manuel R. S. Tavares, Vasileios Belagiannis, João Paulo Papa, Jacinto C. Nascimento, Marco Loog, Zhi Lu, Jaime S. Cardoso, Julien Cornebise.

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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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: 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)
Format: eBook
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Series:Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 10008
Springer eBook Collection.
Subjects:
Optical data processing.
Pattern recognition.
Artificial intelligence.
Computer graphics.
Health informatics.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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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