Medical Computer Vision: Algorithms for Big Data : International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, . Vision, Pattern Recognition, and Graphics)

ISBN 10: 3319139711 ISBN 13: 9783319139715

Editorial: Springer, 2014

Idioma: Ingl�s

Condici�n: Nuevo Encuadernaci�n de tapa blanda

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Detalles bibliogr�ficos

T�tulo: Medical Computer Vision: Algorithms for Big Data : International Workshop, MCV 2014, Held in Conjunction with MICCAI 2014, Cambridge, MA, USA, . Vision, Pattern Recognition, and Graphics)
Editor: Springer
Fecha de publicaci�n: 2014
Idioma: Ingl�s
ISBN 10: 3319139711
ISBN 13: 9783319139715
Encuadernaci�n: Encuadernaci�n de tapa blanda
Estado: New

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Sinopsis

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.

Rese�a del editor

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.

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