Domain generalization in deep learning for contrast-enhanced imaging

•Domain generalization techniques are compared in a multi-center scenario.•Extensive data augmentation improves model generalization across centers.•Image harmonization methods underperform compared to domain generalization methods.•Single-domain models can be finetuned to reach and even surpass multi-center models.•Transfer learning results in both efficient training and competitive performance.