Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI

Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI

Deep learning (DL) has shown great potential in medical image enhancement problems, such as super-resolution or image synthesis. However, to date, most existing approaches are based on deterministic models, neglecting the presence of different sources of uncertainty in such problems. Here we introdu...

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Bibliographic Details
Main Authors: Ryutaro Tanno, Daniel E. Worrall, Enrico Kaden, Aurobrata Ghosh, Francesco Grussu, Alberto Bizzi, Stamatios N. Sotiropoulos, Antonio Criminisi, Daniel C. Alexander
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:NeuroImage
Subjects:
Uncertainty quantification
Deep learning
Safety
Robustness
Interpretability
Super-resolution
Online Access:http://www.sciencedirect.com/science/article/pii/S1053811920308521

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http://www.sciencedirect.com/science/article/pii/S1053811920308521

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