Deep Learning and Data Labeling for Medical Applications

. Ed(s): Carneiro, Gustavo; Mateus, Diana; Peter, Loic; Belagiannis, Vasileios; Bradley, Andrew; Tavares, Joao Manuel R. S.; Loog, Marco; Cardoso, Ja

ISBN 10: 3319469754 / ISBN 13: 9783319469751

Published by Springer International Publishing AG, 2016

About this Item

Editor(s): Carneiro, Gustavo; Mateus, Diana; Peter, Loic; Belagiannis, Vasileios; Bradley, Andrew; Tavares, Joao Manuel R. S.; Loog, Marco; Cardoso, Jaime S. Series: Lecture Notes in Computer Science. Num Pages: 280 pages, 115 black & white illustrations, biography. BIC Classification: UBH; UML; UYQ; UYQP; UYT. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 16. Weight in Grams: 456. . 2016. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9783319469751

Bibliographic Details

• Title: Deep Learning and Data Labeling for Medical Applications
• Publisher: Springer International Publishing AG
• Publication Date: 2016
• Language: English
• ISBN 10: 3319469754
• ISBN 13: 9783319469751
• Binding: Soft cover
• Condition: New

About this title

Synopsis

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.