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

Volume: 225, Pages: 117366 - 117366
Published: Jan 1, 2021
Abstract
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 introduce methods to characterise different components of uncertainty, and demonstrate the ideas using diffusion MRI super-resolution. Specifically, we...
Paper Details
Title
Uncertainty modelling in deep learning for safer neuroimage enhancement: Demonstration in diffusion MRI
Published Date
Jan 1, 2021
Journal
Volume
225
Pages
117366 - 117366
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.