Machine Learning in Medical Imaging - Info and Reading Options
Third International Workshop, MLMI 2012, Held in Conjunction with MICCAI 2012, Nice, France, October 1, 2012, Revised Selected Papers
By Fei Wang
"Machine Learning in Medical Imaging" was published by Springer Berlin Heidelberg in 2012 - Berlin, Heidelberg, it has 276 pages and the language of the book is English.
“Machine Learning in Medical Imaging” Metadata:
- Title: ➤ Machine Learning in Medical Imaging
- Author: Fei Wang
- Language: English
- Number of Pages: 276
- Publisher: Springer Berlin Heidelberg
- Publish Date: 2012
- Publish Location: Berlin, Heidelberg
“Machine Learning in Medical Imaging” Subjects and Themes:
- Subjects: ➤ Computer Imaging, Vision, Pattern Recognition and Graphics - Pattern perception - Image Processing and Computer Vision - Computer graphics - Database management - Optical pattern recognition - Computer vision - Computer science - Artificial intelligence - Artificial Intelligence (incl. Robotics) - Machine learning - Congresses - Diagnostic imaging - Data processing - Medical applications - Image Interpretation, Computer-Assisted - Methods - Automated Pattern Recognition
Edition Specifications:
- Format: [electronic resource] :
- Pagination: XII, 276 p. 91 illus.
Edition Identifiers:
- The Open Library ID: OL27072048M - OL19884976W
- Library of Congress Control Number (LCCN): 2012953042
- ISBN-13: 9783642354281
- All ISBNs: 9783642354281
AI-generated Review of “Machine Learning in Medical Imaging”:
"Machine Learning in Medical Imaging" Description:
The Open Library:
This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Medical Imaging, MLMI 2012, held in conjunction with MICCAI 2012, in Nice, France, in October 2012. <br>The 33 revised full papers presented were carefully reviewed and selected from 67 submissions. The main aim of this workshop is to help advance the scientific research within the broad field of machine learning in medical imaging. It focuses on major trends and challenges in this area, and it presents work aimed to identify new cutting-edge techniques and their use in medical imaging.
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