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

Carneiro, Gustavo (Editor)/ Mateus, Diana (Editor)/ Loïc, Peter (Editor)/ Bradley, Andrew (Editor)/ Tavares, João Manuel R. S. (Editor)

ISBN 10: 3319469754 / ISBN 13: 9783319469751

Editore: Springer-Verlag New York Inc, 2016

Riguardo questo articolo

277 pages. 9.00x6.00x0.75 inches. In Stock.

Codice articolo x-3319469754

Dati bibliografici

• Titolo: 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
• Editore: Springer-Verlag New York Inc
• Data di pubblicazione: 2016
• Lingua: Inglese
• ISBN 10: 3319469754
• ISBN 13: 9783319469751
• Rilegatura: Paperback
• Condizione: Brand New
• Peso: 4,52 chilogrammi

Descrizione articolo

Riassunto

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.