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Medical Content-Based Retrieval for Clinical Decision Support Second MICCAI International Workshop, MCBR-CDS 2011, Toronto, ON, Canada, September 22, 2011, Revised Selected Papers by Henning MГјller

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Published by Springer Berlin Heidelberg in Berlin, Heidelberg .
Written in English

Subjects:

  • Biometric identification,
  • Pattern perception,
  • Information Systems,
  • Data Mining and Knowledge Discovery,
  • Information organization,
  • Optical pattern recognition,
  • Information storage and retrieval systems,
  • Information retrieval,
  • Computer Imaging, Vision, Pattern Recognition and Graphics,
  • Computer vision,
  • Computer science,
  • Management of Computing and Information Systems,
  • Data mining,
  • Biometrics

Book details:

Edition Notes

Statementedited by Henning Müller, Hayit Greenspan, Tanveer Syeda-Mahmood
SeriesLecture Notes in Computer Science -- 7075
ContributionsGreenspan, Hayit, Syeda-Mahmood, Tanveer, SpringerLink (Online service)
The Physical Object
Format[electronic resource] :
ID Numbers
Open LibraryOL27074081M
ISBN 109783642284601

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About this book This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS , held in Toronto, Canada, in September The 11 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 17 submissions.   Description This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS , held in Nice, France, in October The 10 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 15 submissions. This book constitutes the refereed proceedings of the Second MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support, MCBR-CBS , held in . The goal of the workshop was to show the application of content-based retrieval in clinical decision support. With advances in electronic patient record systems, a large number of pre-diagnosed patient data sets are now bec- ing : Paperback.

Abstract In this paper, we provide an overview of the first workshop on Medical Content–Based Retrieval for Clinical Decision Support (MCBR–CDS), which was held in conjunction with the Medical.   [PDF] Medical Content-Based Retrieval for Clinical Decision Support: First MICCAI International. In several articles, content-based access to medical images for supporting clinical decision-making has been proposed that would ease the management of clinical data and scenarios for the integration of content-based access methods into picture archiving and . MICCAI workshop on Medical Content-based Retrieval for Clinical Decision Support (MCBR-CDS) | October Download BibTex The objective of this work is a scalable, real-time, visual search engine for 3-D medical images, where a user is able to select a query Region Of Interest (ROI) and automatically detect the corresponding regions within all.

This book constitutes the refereed proceedings of the Third MICCAI Workshop on Medical Content-Based more» Retrieval for Clinical Decision Support, MCBR .   Clinical decision support for evidence-based care. Dudley Maude. Follow. 5 years ago | 3 views. Clinical decision support for evidence-based care. Report. Browse more videos. Playing next. [PDF Download] Evidence-Based Emergency Care: Diagnostic Testing and Clinical Decision Rules. It is an essential for the inquisitive mind, seeking to understand the scope of medical imaging in clinical practice. Contents Preface Section 1 General Perspectives in Medical Imaging 1 Content Based Retrieval Systems in a Clinical Context 2 Challenges and Peculiarities of Paediatric Imaging 3 Clinical Applications of Nuclear Medicine. The potential benefits of content-based medical image retrieval (CBMIR) range from clinical decision support to medical education and research. Diagnosis by comparing past and current medical images associated with pathological conditions has become one of the primary approaches in case-based reasoning.