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

Book (2016)

Contributors

G. Carneiro University of Adelaide (editor)
D. Mateus Technische Universität München (editor)
J.S. Cardoso Universidade do Porto (editor)
J. Cornebise Google DeepMind (editor)
L. Peter Technische Universität München (editor)
A. Bradley University of Queensland (editor)
J.M.R.S. Tavares Universidade do Porto (editor)
V. Belagiannis University of Oxford (editor)
J.P. Papa Universidade Estadual Paulista (editor)
J.C. Nascimento Instituto Superior Técnico (IST) (editor)
M. Loog Pattern Recognition and Bioinformatics - EEMCS (editor)
Z Lu University of South Australia (editor)
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
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Published Date

2016

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

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 Synthesis, LABELS 2016, and the Second International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2016. The 28 revised regular papers presented in this book were carefully reviewed and selected from a total of 52 submissions. The 7 papers selected for LABELS deal with topics from the following fields: crowd-sourcing methods; active learning; transfer learning; semi-supervised learning; and modeling of label uncertainty.The 21 papers selected for DLMIA span a wide range of topics such as 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; and applications based on deep learning techniques