Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, Denoising
Motivation: Developing denoising deep learning models that can generalize effectively across diverse MRI test distributions, overcoming limitations posed by data scarcity in training datasets.
Goal(s): To determine if joint supervised and self-supervised learning can outperform traditional fully supervised (FS) training on Out-Of-Distribution (OOD) MRI image denoising problems.
Approach: We employ a joint training methodology that combines both FS and self-supervised contrastive loss terms for MRI image denoising. This approach is evaluated for its robustness to several OOD test datasets.
Results: Our joint training approach outperforms FS learning on OOD image denoising problems at low noise levels; however, its efficacy diminishes at higher noise levels.
Impact: This research demonstrates improved denoising performance on low-noise OOD MRI data, addressing a key challenge in generalizing to diverse imaging conditions with limited training data.
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