deep learning data labeling (4 Ergebnisse)

Zustand: New. 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.

Paperback. Zustand: Brand New. 277 pages. 9.00x6.00x0.75 inches. In Stock.

Taschenbuch. Zustand: Neu. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 296 pp. Englisch.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.