UNITS framework proves self-supervised splitting risk in MRI reconstruction is a weighted supervised risk, yielding identical Bayes-optimal predictors and relating training residuals to prediction bias.
Physics-driven deep learning for computational magnetic resonance imaging: Combining physics and machine learning for improved medical imaging,
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Towards a Unified Theoretical Framework for Splitting-based Self-Supervised MRI Reconstruction
UNITS framework proves self-supervised splitting risk in MRI reconstruction is a weighted supervised risk, yielding identical Bayes-optimal predictors and relating training residuals to prediction bias.