Understanding and Interpreting Machine Learning in Medical Image Computing Applications (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
149
Utgivningsdatum
2018-10-24
Upplaga
1st ed. 2018
Förlag
Springer Nature Switzerland AG
Medarbetare
Martel, Anne (red.)/Maier-Hein, Lena (red.)/Marquand, Andre F. (red.)/Duchesnay, Edouard (red.)/Lofstedt, Tommy (red.)/Landman, Bennett (red.)/Cardoso, M. Jorge (red.)/Silva, Carlos A. (red.)/Pereira, Sergio (red.)/Meier, Raphael (red.)
Illustratör/Fotograf
Bibliographie
Illustrationer
60 Illustrations, black and white; XVI, 149 p. 60 illus.
Dimensioner
234 x 156 x 9 mm
Vikt
245 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030026271
Understanding and Interpreting Machine Learning in Medical Image Computing Applications (häftad)

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

Häftad Engelska, 2018-10-24
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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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