An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images
This work introduces an unsupervised and interpretable QA framework for multi-contrast MR images that quantifies artifact severity, and employs an unsupervised contrastive learning approach to train the model without requiring manual labels or preprocessing.