Overview
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11905)
Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)
Included in the following conference series:
Conference proceedings info: MLMIR 2019.
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Table of contents (25 papers)
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Deep Learning for Magnetic Resonance Imaging
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Deep Learning for Computed Tomography
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Deep Learning for General Image Reconstruction
Other volumes
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Machine Learning for Medical Image Reconstruction
Keywords
About this book
This book constitutes the refereed proceedings of the Second International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019.
The 24 full papers presented were carefully reviewed and selected from 32 submissions. The papers are organized in the following topical sections: deep learning for magnetic resonance imaging; deep learning for computed tomography; and deep learning for general image reconstruction.
Editors and Affiliations
Bibliographic Information
Book Title: Machine Learning for Medical Image Reconstruction
Book Subtitle: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings
Editors: Florian Knoll, Andreas Maier, Daniel Rueckert, Jong Chul Ye
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-030-33843-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-33842-8Published: 24 October 2019
eBook ISBN: 978-3-030-33843-5Published: 24 October 2019
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: IX, 266
Number of Illustrations: 34 b/w illustrations, 94 illustrations in colour
Topics: Artificial Intelligence, Computers and Education, Computer Appl. in Social and Behavioral Sciences, Computational Biology/Bioinformatics, Image Processing and Computer Vision, Health Informatics