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Abstract #3829

Robust domain adaptation for transferable MRI denoising models

Clara Zaki1,2, Charles Millard3, and Mark Chiew1,2
1Medical Biophysics, University of Toronto, Toronto, ON, Canada, 2Physical Sciences Research Platform, Sunnybrook Research Institute, Toronto, ON, Canada, 3FMRIB, Wellcome Centre for Integrative Neuroimaging, Oxford, United Kingdom

Synopsis

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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Keywords