Adapting image editing foundation models via LoRA with multi-reference conditioning achieves state-of-the-art CT metal artifact reduction using two orders of magnitude less paired training data than prior methods.
Xcist—an open access x-ray/ct simulation toolkit.Physics in Medicine &; Biology, 67(19): 194002
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Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction
Adapting image editing foundation models via LoRA with multi-reference conditioning achieves state-of-the-art CT metal artifact reduction using two orders of magnitude less paired training data than prior methods.