Bayesian and Graphical Models for Biomedical Imaging

. Ed(s): Cardoso, M. Jorge; Simpson, Ivor; Arbel, Tal; Precup, Doina; Ribbens, Annemie

ISBN 10: 3319122886 / ISBN 13: 9783319122885

Published by Springer International Publishing AG, 2014

About this Item

Editor(s): Cardoso, M. Jorge; Simpson, Ivor; Arbel, Tal; Precup, Doina; Ribbens, Annemie. Series: Lecture Notes in Computer Science / Theoretical Computer Science and General Issues. Num Pages: 141 pages, 54 black & white illustrations, biography. BIC Classification: PBD; UMB; UML; UYQ; UYQP; UYT. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 8. Weight in Grams: 232. . 2014. Paperback. . . . . Books ship from the US and Ireland.

Seller Inventory # V9783319122885

Bibliographic Details

• Title: Bayesian and Graphical Models for Biomedical Imaging
• Publisher: Springer International Publishing AG
• Publication Date: 2014
• Language: English
• ISBN 10: 3319122886
• ISBN 13: 9783319122885
• Binding: Soft cover
• Condition: New

About this title

Synopsis

This book constitutes the refereed proceedings of the First International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2014, held in Cambridge, MA, USA, in September 2014 as a satellite event of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014.
The 11 revised full papers presented were carefully reviewed and selected from numerous submissions with a key aspect on probabilistic modeling applied to medical image analysis. The objectives of this workshop compared to other workshops, e.g. machine learning in medical imaging, have a stronger mathematical focus on the foundations of probabilistic modeling and inference. The papers highlight the potential of using Bayesian or random field graphical models for advancing scientific research in biomedical image analysis or for the advancement of modeling and analysis of medical imaging data.