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Understanding and Interpreting Machine Learning in Medical Image Computing Applications

First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings

Language EnglishEnglish
Book Paperback
Book Understanding and Interpreting Machine Learning in Medical Image Computing Applications Danail Stoyanov
Libristo code: 20235166
Publishers Springer Nature Switzerland AG, October 2018
This book constitutes the refereed joint proceedings of the First International Workshop on Machine... Full description
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This book constitutes the refereed joint proceedings of the First International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2018, the First International Workshop on Deep Learning Fails, DLF 2018, and the First International Workshop on Interpretability of Machine Intelligence in Medical Image Computing, iMIMIC 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 4 full MLCN papers, the 6 full DLF papers, and the 6 full iMIMIC papers included in this volume were carefully reviewed and selected. The MLCN contributions develop state-of-the-art machine learning methods such as spatio-temporal Gaussian process analysis, stochastic variational inference, and deep learning for applications in Alzheimer's disease diagnosis and multi-site neuroimaging data analysis; the DLF papers evaluate the strengths and weaknesses of DL and identify the main challenges in the current state of the art and future directions; the iMIMIC papers cover a large range of topics in the field of interpretability of machine learning in the context of medical image analysis.

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About the book

Full name Understanding and Interpreting Machine Learning in Medical Image Computing Applications
Language English
Binding Book - Paperback
Date of issue 2018
Number of pages 149
EAN 9783030026271
Libristo code 20235166
Weight 266
Dimensions 155 x 235 x 10
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