1 option
Machine Learning for Medical Image Reconstruction : First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings / edited by Florian Knoll, Andreas Maier, Daniel Rueckert.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 11074
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 11074
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computer vision.
- Computer networks.
- Logic design.
- Medical informatics.
- Artificial Intelligence.
- Computer Vision.
- Computer Communication Networks.
- Logic Design.
- Health Informatics.
- Local Subjects:
- Artificial Intelligence.
- Computer Vision.
- Computer Communication Networks.
- Logic Design.
- Health Informatics.
- Physical Description:
- 1 online resource (X, 158 pages) : 67 illustrations
- Edition:
- 1st ed. 2018.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2018.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the First International Workshop on Machine Learning for Medical Reconstruction, MLMIR 2018, held in conjunction with MICCAI 2018, in Granada, Spain, in September 2018. The 17 full papers presented were carefully reviewed and selected from 21 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.
- Contents:
- Deep learning for magnetic resonance imaging
- Deep learning for computed tomography
- Deep learning for general image reconstruction.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-00129-2
- 9783030001292
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.