Medical Computer Vision: Algorithms for Big Data

Bjoern Menze

ISBN 10: 3319139711 / ISBN 13: 9783319139715

Published by Springer International Publishing, Springer International Publishing Dez 2014, 2014

About this Item

Neuware -This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCV 2014, held in Cambridge, MA, USA, in September 2019, in conjunction with the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014. The one-day workshop aimed at exploring the use of modern computer vision technology and 'big data' algorithms in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The 18 full and 1 short papers presented in this volume were carefully reviewed and selected from 30 submission.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 224 pp. Englisch.

Seller Inventory # 9783319139715

Bibliographic Details

• Title: Medical Computer Vision: Algorithms for Big Data
• Publisher: Springer International Publishing, Springer International Publishing Dez 2014
• Publication Date: 2014
• Language: English
• ISBN 10: 3319139711
• ISBN 13: 9783319139715
• Binding: Taschenbuch
• Condition: Neu

About this title

Synopsis

This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision: Algorithms for Big Data, MCV 2014, held in Cambridge, MA, USA, in September 2019, in conjunction with the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014. The one-day workshop aimed at exploring the use of modern computer vision technology and "big data" algorithms in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies emphasizing questions of harvesting, organizing and learning from large-scale medical imaging data sets and general-purpose automatic understanding of medical images. The 18 full and 1 short papers presented in this volume were carefully reviewed and selected from 30 submission.